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<FONT color="green">001</FONT>    /*<a name="line.1"></a>
<FONT color="green">002</FONT>     * Licensed to the Apache Software Foundation (ASF) under one or more<a name="line.2"></a>
<FONT color="green">003</FONT>     * contributor license agreements.  See the NOTICE file distributed with<a name="line.3"></a>
<FONT color="green">004</FONT>     * this work for additional information regarding copyright ownership.<a name="line.4"></a>
<FONT color="green">005</FONT>     * The ASF licenses this file to You under the Apache License, Version 2.0<a name="line.5"></a>
<FONT color="green">006</FONT>     * (the "License"); you may not use this file except in compliance with<a name="line.6"></a>
<FONT color="green">007</FONT>     * the License.  You may obtain a copy of the License at<a name="line.7"></a>
<FONT color="green">008</FONT>     *<a name="line.8"></a>
<FONT color="green">009</FONT>     *      http://www.apache.org/licenses/LICENSE-2.0<a name="line.9"></a>
<FONT color="green">010</FONT>     *<a name="line.10"></a>
<FONT color="green">011</FONT>     * Unless required by applicable law or agreed to in writing, software<a name="line.11"></a>
<FONT color="green">012</FONT>     * distributed under the License is distributed on an "AS IS" BASIS,<a name="line.12"></a>
<FONT color="green">013</FONT>     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.<a name="line.13"></a>
<FONT color="green">014</FONT>     * See the License for the specific language governing permissions and<a name="line.14"></a>
<FONT color="green">015</FONT>     * limitations under the License.<a name="line.15"></a>
<FONT color="green">016</FONT>     */<a name="line.16"></a>
<FONT color="green">017</FONT>    package org.apache.commons.math3.stat.regression;<a name="line.17"></a>
<FONT color="green">018</FONT>    <a name="line.18"></a>
<FONT color="green">019</FONT>    import java.util.Arrays;<a name="line.19"></a>
<FONT color="green">020</FONT>    import org.apache.commons.math3.exception.util.LocalizedFormats;<a name="line.20"></a>
<FONT color="green">021</FONT>    import org.apache.commons.math3.util.FastMath;<a name="line.21"></a>
<FONT color="green">022</FONT>    import org.apache.commons.math3.util.Precision;<a name="line.22"></a>
<FONT color="green">023</FONT>    import org.apache.commons.math3.util.MathArrays;<a name="line.23"></a>
<FONT color="green">024</FONT>    <a name="line.24"></a>
<FONT color="green">025</FONT>    /**<a name="line.25"></a>
<FONT color="green">026</FONT>     * This class is a concrete implementation of the {@link UpdatingMultipleLinearRegression} interface.<a name="line.26"></a>
<FONT color="green">027</FONT>     *<a name="line.27"></a>
<FONT color="green">028</FONT>     * &lt;p&gt;The algorithm is described in: &lt;pre&gt;<a name="line.28"></a>
<FONT color="green">029</FONT>     * Algorithm AS 274: Least Squares Routines to Supplement Those of Gentleman<a name="line.29"></a>
<FONT color="green">030</FONT>     * Author(s): Alan J. Miller<a name="line.30"></a>
<FONT color="green">031</FONT>     * Source: Journal of the Royal Statistical Society.<a name="line.31"></a>
<FONT color="green">032</FONT>     * Series C (Applied Statistics), Vol. 41, No. 2<a name="line.32"></a>
<FONT color="green">033</FONT>     * (1992), pp. 458-478<a name="line.33"></a>
<FONT color="green">034</FONT>     * Published by: Blackwell Publishing for the Royal Statistical Society<a name="line.34"></a>
<FONT color="green">035</FONT>     * Stable URL: http://www.jstor.org/stable/2347583 &lt;/pre&gt;&lt;/p&gt;<a name="line.35"></a>
<FONT color="green">036</FONT>     *<a name="line.36"></a>
<FONT color="green">037</FONT>     * &lt;p&gt;This method for multiple regression forms the solution to the OLS problem<a name="line.37"></a>
<FONT color="green">038</FONT>     * by updating the QR decomposition as described by Gentleman.&lt;/p&gt;<a name="line.38"></a>
<FONT color="green">039</FONT>     *<a name="line.39"></a>
<FONT color="green">040</FONT>     * @version $Id: MillerUpdatingRegression.java 1392358 2012-10-01 14:41:55Z psteitz $<a name="line.40"></a>
<FONT color="green">041</FONT>     * @since 3.0<a name="line.41"></a>
<FONT color="green">042</FONT>     */<a name="line.42"></a>
<FONT color="green">043</FONT>    public class MillerUpdatingRegression implements UpdatingMultipleLinearRegression {<a name="line.43"></a>
<FONT color="green">044</FONT>    <a name="line.44"></a>
<FONT color="green">045</FONT>        /** number of variables in regression */<a name="line.45"></a>
<FONT color="green">046</FONT>        private final int nvars;<a name="line.46"></a>
<FONT color="green">047</FONT>        /** diagonals of cross products matrix */<a name="line.47"></a>
<FONT color="green">048</FONT>        private final double[] d;<a name="line.48"></a>
<FONT color="green">049</FONT>        /** the elements of the R`Y */<a name="line.49"></a>
<FONT color="green">050</FONT>        private final double[] rhs;<a name="line.50"></a>
<FONT color="green">051</FONT>        /** the off diagonal portion of the R matrix */<a name="line.51"></a>
<FONT color="green">052</FONT>        private final double[] r;<a name="line.52"></a>
<FONT color="green">053</FONT>        /** the tolerance for each of the variables */<a name="line.53"></a>
<FONT color="green">054</FONT>        private final double[] tol;<a name="line.54"></a>
<FONT color="green">055</FONT>        /** residual sum of squares for all nested regressions */<a name="line.55"></a>
<FONT color="green">056</FONT>        private final double[] rss;<a name="line.56"></a>
<FONT color="green">057</FONT>        /** order of the regressors */<a name="line.57"></a>
<FONT color="green">058</FONT>        private final int[] vorder;<a name="line.58"></a>
<FONT color="green">059</FONT>        /** scratch space for tolerance calc */<a name="line.59"></a>
<FONT color="green">060</FONT>        private final double[] work_tolset;<a name="line.60"></a>
<FONT color="green">061</FONT>        /** number of observations entered */<a name="line.61"></a>
<FONT color="green">062</FONT>        private long nobs = 0;<a name="line.62"></a>
<FONT color="green">063</FONT>        /** sum of squared errors of largest regression */<a name="line.63"></a>
<FONT color="green">064</FONT>        private double sserr = 0.0;<a name="line.64"></a>
<FONT color="green">065</FONT>        /** has rss been called? */<a name="line.65"></a>
<FONT color="green">066</FONT>        private boolean rss_set = false;<a name="line.66"></a>
<FONT color="green">067</FONT>        /** has the tolerance setting method been called */<a name="line.67"></a>
<FONT color="green">068</FONT>        private boolean tol_set = false;<a name="line.68"></a>
<FONT color="green">069</FONT>        /** flags for variables with linear dependency problems */<a name="line.69"></a>
<FONT color="green">070</FONT>        private final boolean[] lindep;<a name="line.70"></a>
<FONT color="green">071</FONT>        /** singular x values */<a name="line.71"></a>
<FONT color="green">072</FONT>        private final double[] x_sing;<a name="line.72"></a>
<FONT color="green">073</FONT>        /** workspace for singularity method */<a name="line.73"></a>
<FONT color="green">074</FONT>        private final double[] work_sing;<a name="line.74"></a>
<FONT color="green">075</FONT>        /** summation of Y variable */<a name="line.75"></a>
<FONT color="green">076</FONT>        private double sumy = 0.0;<a name="line.76"></a>
<FONT color="green">077</FONT>        /** summation of squared Y values */<a name="line.77"></a>
<FONT color="green">078</FONT>        private double sumsqy = 0.0;<a name="line.78"></a>
<FONT color="green">079</FONT>        /** boolean flag whether a regression constant is added */<a name="line.79"></a>
<FONT color="green">080</FONT>        private boolean hasIntercept;<a name="line.80"></a>
<FONT color="green">081</FONT>        /** zero tolerance */<a name="line.81"></a>
<FONT color="green">082</FONT>        private final double epsilon;<a name="line.82"></a>
<FONT color="green">083</FONT>        /**<a name="line.83"></a>
<FONT color="green">084</FONT>         *  Set the default constructor to private access<a name="line.84"></a>
<FONT color="green">085</FONT>         *  to prevent inadvertent instantiation<a name="line.85"></a>
<FONT color="green">086</FONT>         */<a name="line.86"></a>
<FONT color="green">087</FONT>        @SuppressWarnings("unused")<a name="line.87"></a>
<FONT color="green">088</FONT>        private MillerUpdatingRegression() {<a name="line.88"></a>
<FONT color="green">089</FONT>            this(-1, false, Double.NaN);<a name="line.89"></a>
<FONT color="green">090</FONT>        }<a name="line.90"></a>
<FONT color="green">091</FONT>    <a name="line.91"></a>
<FONT color="green">092</FONT>        /**<a name="line.92"></a>
<FONT color="green">093</FONT>         * This is the augmented constructor for the MillerUpdatingRegression class.<a name="line.93"></a>
<FONT color="green">094</FONT>         *<a name="line.94"></a>
<FONT color="green">095</FONT>         * @param numberOfVariables number of regressors to expect, not including constant<a name="line.95"></a>
<FONT color="green">096</FONT>         * @param includeConstant include a constant automatically<a name="line.96"></a>
<FONT color="green">097</FONT>         * @param errorTolerance  zero tolerance, how machine zero is determined<a name="line.97"></a>
<FONT color="green">098</FONT>         * @throws ModelSpecificationException if {@code numberOfVariables is less than 1}<a name="line.98"></a>
<FONT color="green">099</FONT>         */<a name="line.99"></a>
<FONT color="green">100</FONT>        public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant, double errorTolerance)<a name="line.100"></a>
<FONT color="green">101</FONT>        throws ModelSpecificationException {<a name="line.101"></a>
<FONT color="green">102</FONT>            if (numberOfVariables &lt; 1) {<a name="line.102"></a>
<FONT color="green">103</FONT>                throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);<a name="line.103"></a>
<FONT color="green">104</FONT>            }<a name="line.104"></a>
<FONT color="green">105</FONT>            if (includeConstant) {<a name="line.105"></a>
<FONT color="green">106</FONT>                this.nvars = numberOfVariables + 1;<a name="line.106"></a>
<FONT color="green">107</FONT>            } else {<a name="line.107"></a>
<FONT color="green">108</FONT>                this.nvars = numberOfVariables;<a name="line.108"></a>
<FONT color="green">109</FONT>            }<a name="line.109"></a>
<FONT color="green">110</FONT>            this.hasIntercept = includeConstant;<a name="line.110"></a>
<FONT color="green">111</FONT>            this.nobs = 0;<a name="line.111"></a>
<FONT color="green">112</FONT>            this.d = new double[this.nvars];<a name="line.112"></a>
<FONT color="green">113</FONT>            this.rhs = new double[this.nvars];<a name="line.113"></a>
<FONT color="green">114</FONT>            this.r = new double[this.nvars * (this.nvars - 1) / 2];<a name="line.114"></a>
<FONT color="green">115</FONT>            this.tol = new double[this.nvars];<a name="line.115"></a>
<FONT color="green">116</FONT>            this.rss = new double[this.nvars];<a name="line.116"></a>
<FONT color="green">117</FONT>            this.vorder = new int[this.nvars];<a name="line.117"></a>
<FONT color="green">118</FONT>            this.x_sing = new double[this.nvars];<a name="line.118"></a>
<FONT color="green">119</FONT>            this.work_sing = new double[this.nvars];<a name="line.119"></a>
<FONT color="green">120</FONT>            this.work_tolset = new double[this.nvars];<a name="line.120"></a>
<FONT color="green">121</FONT>            this.lindep = new boolean[this.nvars];<a name="line.121"></a>
<FONT color="green">122</FONT>            for (int i = 0; i &lt; this.nvars; i++) {<a name="line.122"></a>
<FONT color="green">123</FONT>                vorder[i] = i;<a name="line.123"></a>
<FONT color="green">124</FONT>            }<a name="line.124"></a>
<FONT color="green">125</FONT>            if (errorTolerance &gt; 0) {<a name="line.125"></a>
<FONT color="green">126</FONT>                this.epsilon = errorTolerance;<a name="line.126"></a>
<FONT color="green">127</FONT>            } else {<a name="line.127"></a>
<FONT color="green">128</FONT>                this.epsilon = -errorTolerance;<a name="line.128"></a>
<FONT color="green">129</FONT>            }<a name="line.129"></a>
<FONT color="green">130</FONT>        }<a name="line.130"></a>
<FONT color="green">131</FONT>    <a name="line.131"></a>
<FONT color="green">132</FONT>        /**<a name="line.132"></a>
<FONT color="green">133</FONT>         * Primary constructor for the MillerUpdatingRegression.<a name="line.133"></a>
<FONT color="green">134</FONT>         *<a name="line.134"></a>
<FONT color="green">135</FONT>         * @param numberOfVariables maximum number of potential regressors<a name="line.135"></a>
<FONT color="green">136</FONT>         * @param includeConstant include a constant automatically<a name="line.136"></a>
<FONT color="green">137</FONT>         * @throws ModelSpecificationException if {@code numberOfVariables is less than 1}<a name="line.137"></a>
<FONT color="green">138</FONT>         */<a name="line.138"></a>
<FONT color="green">139</FONT>        public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant)<a name="line.139"></a>
<FONT color="green">140</FONT>        throws ModelSpecificationException {<a name="line.140"></a>
<FONT color="green">141</FONT>            this(numberOfVariables, includeConstant, Precision.EPSILON);<a name="line.141"></a>
<FONT color="green">142</FONT>        }<a name="line.142"></a>
<FONT color="green">143</FONT>    <a name="line.143"></a>
<FONT color="green">144</FONT>        /**<a name="line.144"></a>
<FONT color="green">145</FONT>         * A getter method which determines whether a constant is included.<a name="line.145"></a>
<FONT color="green">146</FONT>         * @return true regression has an intercept, false no intercept<a name="line.146"></a>
<FONT color="green">147</FONT>         */<a name="line.147"></a>
<FONT color="green">148</FONT>        public boolean hasIntercept() {<a name="line.148"></a>
<FONT color="green">149</FONT>            return this.hasIntercept;<a name="line.149"></a>
<FONT color="green">150</FONT>        }<a name="line.150"></a>
<FONT color="green">151</FONT>    <a name="line.151"></a>
<FONT color="green">152</FONT>        /**<a name="line.152"></a>
<FONT color="green">153</FONT>         * Gets the number of observations added to the regression model.<a name="line.153"></a>
<FONT color="green">154</FONT>         * @return number of observations<a name="line.154"></a>
<FONT color="green">155</FONT>         */<a name="line.155"></a>
<FONT color="green">156</FONT>        public long getN() {<a name="line.156"></a>
<FONT color="green">157</FONT>            return this.nobs;<a name="line.157"></a>
<FONT color="green">158</FONT>        }<a name="line.158"></a>
<FONT color="green">159</FONT>    <a name="line.159"></a>
<FONT color="green">160</FONT>        /**<a name="line.160"></a>
<FONT color="green">161</FONT>         * Adds an observation to the regression model.<a name="line.161"></a>
<FONT color="green">162</FONT>         * @param x the array with regressor values<a name="line.162"></a>
<FONT color="green">163</FONT>         * @param y  the value of dependent variable given these regressors<a name="line.163"></a>
<FONT color="green">164</FONT>         * @exception ModelSpecificationException if the length of {@code x} does not equal<a name="line.164"></a>
<FONT color="green">165</FONT>         * the number of independent variables in the model<a name="line.165"></a>
<FONT color="green">166</FONT>         */<a name="line.166"></a>
<FONT color="green">167</FONT>        public void addObservation(final double[] x, final double y)<a name="line.167"></a>
<FONT color="green">168</FONT>        throws ModelSpecificationException {<a name="line.168"></a>
<FONT color="green">169</FONT>    <a name="line.169"></a>
<FONT color="green">170</FONT>            if ((!this.hasIntercept &amp;&amp; x.length != nvars) ||<a name="line.170"></a>
<FONT color="green">171</FONT>                   (this.hasIntercept &amp;&amp; x.length + 1 != nvars)) {<a name="line.171"></a>
<FONT color="green">172</FONT>                throw new ModelSpecificationException(LocalizedFormats.INVALID_REGRESSION_OBSERVATION,<a name="line.172"></a>
<FONT color="green">173</FONT>                        x.length, nvars);<a name="line.173"></a>
<FONT color="green">174</FONT>            }<a name="line.174"></a>
<FONT color="green">175</FONT>            if (!this.hasIntercept) {<a name="line.175"></a>
<FONT color="green">176</FONT>                include(MathArrays.copyOf(x, x.length), 1.0, y);<a name="line.176"></a>
<FONT color="green">177</FONT>            } else {<a name="line.177"></a>
<FONT color="green">178</FONT>                final double[] tmp = new double[x.length + 1];<a name="line.178"></a>
<FONT color="green">179</FONT>                System.arraycopy(x, 0, tmp, 1, x.length);<a name="line.179"></a>
<FONT color="green">180</FONT>                tmp[0] = 1.0;<a name="line.180"></a>
<FONT color="green">181</FONT>                include(tmp, 1.0, y);<a name="line.181"></a>
<FONT color="green">182</FONT>            }<a name="line.182"></a>
<FONT color="green">183</FONT>            ++nobs;<a name="line.183"></a>
<FONT color="green">184</FONT>    <a name="line.184"></a>
<FONT color="green">185</FONT>        }<a name="line.185"></a>
<FONT color="green">186</FONT>    <a name="line.186"></a>
<FONT color="green">187</FONT>        /**<a name="line.187"></a>
<FONT color="green">188</FONT>         * Adds multiple observations to the model.<a name="line.188"></a>
<FONT color="green">189</FONT>         * @param x observations on the regressors<a name="line.189"></a>
<FONT color="green">190</FONT>         * @param y observations on the regressand<a name="line.190"></a>
<FONT color="green">191</FONT>         * @throws ModelSpecificationException if {@code x} is not rectangular, does not match<a name="line.191"></a>
<FONT color="green">192</FONT>         * the length of {@code y} or does not contain sufficient data to estimate the model<a name="line.192"></a>
<FONT color="green">193</FONT>         */<a name="line.193"></a>
<FONT color="green">194</FONT>        public void addObservations(double[][] x, double[] y) throws ModelSpecificationException {<a name="line.194"></a>
<FONT color="green">195</FONT>            if ((x == null) || (y == null) || (x.length != y.length)) {<a name="line.195"></a>
<FONT color="green">196</FONT>                throw new ModelSpecificationException(<a name="line.196"></a>
<FONT color="green">197</FONT>                      LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,<a name="line.197"></a>
<FONT color="green">198</FONT>                      (x == null) ? 0 : x.length,<a name="line.198"></a>
<FONT color="green">199</FONT>                      (y == null) ? 0 : y.length);<a name="line.199"></a>
<FONT color="green">200</FONT>            }<a name="line.200"></a>
<FONT color="green">201</FONT>            if (x.length == 0) {  // Must be no y data either<a name="line.201"></a>
<FONT color="green">202</FONT>                throw new ModelSpecificationException(<a name="line.202"></a>
<FONT color="green">203</FONT>                        LocalizedFormats.NO_DATA);<a name="line.203"></a>
<FONT color="green">204</FONT>            }<a name="line.204"></a>
<FONT color="green">205</FONT>            if (x[0].length + 1 &gt; x.length) {<a name="line.205"></a>
<FONT color="green">206</FONT>                throw new ModelSpecificationException(<a name="line.206"></a>
<FONT color="green">207</FONT>                      LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,<a name="line.207"></a>
<FONT color="green">208</FONT>                      x.length, x[0].length);<a name="line.208"></a>
<FONT color="green">209</FONT>            }<a name="line.209"></a>
<FONT color="green">210</FONT>            for (int i = 0; i &lt; x.length; i++) {<a name="line.210"></a>
<FONT color="green">211</FONT>                addObservation(x[i], y[i]);<a name="line.211"></a>
<FONT color="green">212</FONT>            }<a name="line.212"></a>
<FONT color="green">213</FONT>        }<a name="line.213"></a>
<FONT color="green">214</FONT>    <a name="line.214"></a>
<FONT color="green">215</FONT>        /**<a name="line.215"></a>
<FONT color="green">216</FONT>         * The include method is where the QR decomposition occurs. This statement forms all<a name="line.216"></a>
<FONT color="green">217</FONT>         * intermediate data which will be used for all derivative measures.<a name="line.217"></a>
<FONT color="green">218</FONT>         * According to the miller paper, note that in the original implementation the x vector<a name="line.218"></a>
<FONT color="green">219</FONT>         * is overwritten. In this implementation, the include method is passed a copy of the<a name="line.219"></a>
<FONT color="green">220</FONT>         * original data vector so that there is no contamination of the data. Additionally,<a name="line.220"></a>
<FONT color="green">221</FONT>         * this method differs slightly from Gentleman's method, in that the assumption is<a name="line.221"></a>
<FONT color="green">222</FONT>         * of dense design matrices, there is some advantage in using the original gentleman algorithm<a name="line.222"></a>
<FONT color="green">223</FONT>         * on sparse matrices.<a name="line.223"></a>
<FONT color="green">224</FONT>         *<a name="line.224"></a>
<FONT color="green">225</FONT>         * @param x observations on the regressors<a name="line.225"></a>
<FONT color="green">226</FONT>         * @param wi weight of the this observation (-1,1)<a name="line.226"></a>
<FONT color="green">227</FONT>         * @param yi observation on the regressand<a name="line.227"></a>
<FONT color="green">228</FONT>         */<a name="line.228"></a>
<FONT color="green">229</FONT>        private void include(final double[] x, final double wi, final double yi) {<a name="line.229"></a>
<FONT color="green">230</FONT>            int nextr = 0;<a name="line.230"></a>
<FONT color="green">231</FONT>            double w = wi;<a name="line.231"></a>
<FONT color="green">232</FONT>            double y = yi;<a name="line.232"></a>
<FONT color="green">233</FONT>            double xi;<a name="line.233"></a>
<FONT color="green">234</FONT>            double di;<a name="line.234"></a>
<FONT color="green">235</FONT>            double wxi;<a name="line.235"></a>
<FONT color="green">236</FONT>            double dpi;<a name="line.236"></a>
<FONT color="green">237</FONT>            double xk;<a name="line.237"></a>
<FONT color="green">238</FONT>            double _w;<a name="line.238"></a>
<FONT color="green">239</FONT>            this.rss_set = false;<a name="line.239"></a>
<FONT color="green">240</FONT>            sumy = smartAdd(yi, sumy);<a name="line.240"></a>
<FONT color="green">241</FONT>            sumsqy = smartAdd(sumsqy, yi * yi);<a name="line.241"></a>
<FONT color="green">242</FONT>            for (int i = 0; i &lt; x.length; i++) {<a name="line.242"></a>
<FONT color="green">243</FONT>                if (w == 0.0) {<a name="line.243"></a>
<FONT color="green">244</FONT>                    return;<a name="line.244"></a>
<FONT color="green">245</FONT>                }<a name="line.245"></a>
<FONT color="green">246</FONT>                xi = x[i];<a name="line.246"></a>
<FONT color="green">247</FONT>    <a name="line.247"></a>
<FONT color="green">248</FONT>                if (xi == 0.0) {<a name="line.248"></a>
<FONT color="green">249</FONT>                    nextr += nvars - i - 1;<a name="line.249"></a>
<FONT color="green">250</FONT>                    continue;<a name="line.250"></a>
<FONT color="green">251</FONT>                }<a name="line.251"></a>
<FONT color="green">252</FONT>                di = d[i];<a name="line.252"></a>
<FONT color="green">253</FONT>                wxi = w * xi;<a name="line.253"></a>
<FONT color="green">254</FONT>                _w = w;<a name="line.254"></a>
<FONT color="green">255</FONT>                if (di != 0.0) {<a name="line.255"></a>
<FONT color="green">256</FONT>                    dpi = smartAdd(di, wxi * xi);<a name="line.256"></a>
<FONT color="green">257</FONT>                    final double tmp = wxi * xi / di;<a name="line.257"></a>
<FONT color="green">258</FONT>                    if (FastMath.abs(tmp) &gt; Precision.EPSILON) {<a name="line.258"></a>
<FONT color="green">259</FONT>                        w = (di * w) / dpi;<a name="line.259"></a>
<FONT color="green">260</FONT>                    }<a name="line.260"></a>
<FONT color="green">261</FONT>                } else {<a name="line.261"></a>
<FONT color="green">262</FONT>                    dpi = wxi * xi;<a name="line.262"></a>
<FONT color="green">263</FONT>                    w = 0.0;<a name="line.263"></a>
<FONT color="green">264</FONT>                }<a name="line.264"></a>
<FONT color="green">265</FONT>                d[i] = dpi;<a name="line.265"></a>
<FONT color="green">266</FONT>                for (int k = i + 1; k &lt; nvars; k++) {<a name="line.266"></a>
<FONT color="green">267</FONT>                    xk = x[k];<a name="line.267"></a>
<FONT color="green">268</FONT>                    x[k] = smartAdd(xk, -xi * r[nextr]);<a name="line.268"></a>
<FONT color="green">269</FONT>                    if (di != 0.0) {<a name="line.269"></a>
<FONT color="green">270</FONT>                        r[nextr] = smartAdd(di * r[nextr], (_w * xi) * xk) / dpi;<a name="line.270"></a>
<FONT color="green">271</FONT>                    } else {<a name="line.271"></a>
<FONT color="green">272</FONT>                        r[nextr] = xk / xi;<a name="line.272"></a>
<FONT color="green">273</FONT>                    }<a name="line.273"></a>
<FONT color="green">274</FONT>                    ++nextr;<a name="line.274"></a>
<FONT color="green">275</FONT>                }<a name="line.275"></a>
<FONT color="green">276</FONT>                xk = y;<a name="line.276"></a>
<FONT color="green">277</FONT>                y = smartAdd(xk, -xi * rhs[i]);<a name="line.277"></a>
<FONT color="green">278</FONT>                if (di != 0.0) {<a name="line.278"></a>
<FONT color="green">279</FONT>                    rhs[i] = smartAdd(di * rhs[i], wxi * xk) / dpi;<a name="line.279"></a>
<FONT color="green">280</FONT>                } else {<a name="line.280"></a>
<FONT color="green">281</FONT>                    rhs[i] = xk / xi;<a name="line.281"></a>
<FONT color="green">282</FONT>                }<a name="line.282"></a>
<FONT color="green">283</FONT>            }<a name="line.283"></a>
<FONT color="green">284</FONT>            sserr = smartAdd(sserr, w * y * y);<a name="line.284"></a>
<FONT color="green">285</FONT>        }<a name="line.285"></a>
<FONT color="green">286</FONT>    <a name="line.286"></a>
<FONT color="green">287</FONT>        /**<a name="line.287"></a>
<FONT color="green">288</FONT>         * Adds to number a and b such that the contamination due to<a name="line.288"></a>
<FONT color="green">289</FONT>         * numerical smallness of one addend does not corrupt the sum.<a name="line.289"></a>
<FONT color="green">290</FONT>         * @param a - an addend<a name="line.290"></a>
<FONT color="green">291</FONT>         * @param b - an addend<a name="line.291"></a>
<FONT color="green">292</FONT>         * @return the sum of the a and b<a name="line.292"></a>
<FONT color="green">293</FONT>         */<a name="line.293"></a>
<FONT color="green">294</FONT>        private double smartAdd(double a, double b) {<a name="line.294"></a>
<FONT color="green">295</FONT>            final double _a = FastMath.abs(a);<a name="line.295"></a>
<FONT color="green">296</FONT>            final double _b = FastMath.abs(b);<a name="line.296"></a>
<FONT color="green">297</FONT>            if (_a &gt; _b) {<a name="line.297"></a>
<FONT color="green">298</FONT>                final double eps = _a * Precision.EPSILON;<a name="line.298"></a>
<FONT color="green">299</FONT>                if (_b &gt; eps) {<a name="line.299"></a>
<FONT color="green">300</FONT>                    return a + b;<a name="line.300"></a>
<FONT color="green">301</FONT>                }<a name="line.301"></a>
<FONT color="green">302</FONT>                return a;<a name="line.302"></a>
<FONT color="green">303</FONT>            } else {<a name="line.303"></a>
<FONT color="green">304</FONT>                final double eps = _b * Precision.EPSILON;<a name="line.304"></a>
<FONT color="green">305</FONT>                if (_a &gt; eps) {<a name="line.305"></a>
<FONT color="green">306</FONT>                    return a + b;<a name="line.306"></a>
<FONT color="green">307</FONT>                }<a name="line.307"></a>
<FONT color="green">308</FONT>                return b;<a name="line.308"></a>
<FONT color="green">309</FONT>            }<a name="line.309"></a>
<FONT color="green">310</FONT>        }<a name="line.310"></a>
<FONT color="green">311</FONT>    <a name="line.311"></a>
<FONT color="green">312</FONT>        /**<a name="line.312"></a>
<FONT color="green">313</FONT>         * As the name suggests,  clear wipes the internals and reorders everything in the<a name="line.313"></a>
<FONT color="green">314</FONT>         * canonical order.<a name="line.314"></a>
<FONT color="green">315</FONT>         */<a name="line.315"></a>
<FONT color="green">316</FONT>        public void clear() {<a name="line.316"></a>
<FONT color="green">317</FONT>            Arrays.fill(this.d, 0.0);<a name="line.317"></a>
<FONT color="green">318</FONT>            Arrays.fill(this.rhs, 0.0);<a name="line.318"></a>
<FONT color="green">319</FONT>            Arrays.fill(this.r, 0.0);<a name="line.319"></a>
<FONT color="green">320</FONT>            Arrays.fill(this.tol, 0.0);<a name="line.320"></a>
<FONT color="green">321</FONT>            Arrays.fill(this.rss, 0.0);<a name="line.321"></a>
<FONT color="green">322</FONT>            Arrays.fill(this.work_tolset, 0.0);<a name="line.322"></a>
<FONT color="green">323</FONT>            Arrays.fill(this.work_sing, 0.0);<a name="line.323"></a>
<FONT color="green">324</FONT>            Arrays.fill(this.x_sing, 0.0);<a name="line.324"></a>
<FONT color="green">325</FONT>            Arrays.fill(this.lindep, false);<a name="line.325"></a>
<FONT color="green">326</FONT>            for (int i = 0; i &lt; nvars; i++) {<a name="line.326"></a>
<FONT color="green">327</FONT>                this.vorder[i] = i;<a name="line.327"></a>
<FONT color="green">328</FONT>            }<a name="line.328"></a>
<FONT color="green">329</FONT>            this.nobs = 0;<a name="line.329"></a>
<FONT color="green">330</FONT>            this.sserr = 0.0;<a name="line.330"></a>
<FONT color="green">331</FONT>            this.sumy = 0.0;<a name="line.331"></a>
<FONT color="green">332</FONT>            this.sumsqy = 0.0;<a name="line.332"></a>
<FONT color="green">333</FONT>            this.rss_set = false;<a name="line.333"></a>
<FONT color="green">334</FONT>            this.tol_set = false;<a name="line.334"></a>
<FONT color="green">335</FONT>        }<a name="line.335"></a>
<FONT color="green">336</FONT>    <a name="line.336"></a>
<FONT color="green">337</FONT>        /**<a name="line.337"></a>
<FONT color="green">338</FONT>         * This sets up tolerances for singularity testing.<a name="line.338"></a>
<FONT color="green">339</FONT>         */<a name="line.339"></a>
<FONT color="green">340</FONT>        private void tolset() {<a name="line.340"></a>
<FONT color="green">341</FONT>            int pos;<a name="line.341"></a>
<FONT color="green">342</FONT>            double total;<a name="line.342"></a>
<FONT color="green">343</FONT>            final double eps = this.epsilon;<a name="line.343"></a>
<FONT color="green">344</FONT>            for (int i = 0; i &lt; nvars; i++) {<a name="line.344"></a>
<FONT color="green">345</FONT>                this.work_tolset[i] = Math.sqrt(d[i]);<a name="line.345"></a>
<FONT color="green">346</FONT>            }<a name="line.346"></a>
<FONT color="green">347</FONT>            tol[0] = eps * this.work_tolset[0];<a name="line.347"></a>
<FONT color="green">348</FONT>            for (int col = 1; col &lt; nvars; col++) {<a name="line.348"></a>
<FONT color="green">349</FONT>                pos = col - 1;<a name="line.349"></a>
<FONT color="green">350</FONT>                total = work_tolset[col];<a name="line.350"></a>
<FONT color="green">351</FONT>                for (int row = 0; row &lt; col; row++) {<a name="line.351"></a>
<FONT color="green">352</FONT>                    total += Math.abs(r[pos]) * work_tolset[row];<a name="line.352"></a>
<FONT color="green">353</FONT>                    pos += nvars - row - 2;<a name="line.353"></a>
<FONT color="green">354</FONT>                }<a name="line.354"></a>
<FONT color="green">355</FONT>                tol[col] = eps * total;<a name="line.355"></a>
<FONT color="green">356</FONT>            }<a name="line.356"></a>
<FONT color="green">357</FONT>            tol_set = true;<a name="line.357"></a>
<FONT color="green">358</FONT>        }<a name="line.358"></a>
<FONT color="green">359</FONT>    <a name="line.359"></a>
<FONT color="green">360</FONT>        /**<a name="line.360"></a>
<FONT color="green">361</FONT>         * The regcf method conducts the linear regression and extracts the<a name="line.361"></a>
<FONT color="green">362</FONT>         * parameter vector. Notice that the algorithm can do subset regression<a name="line.362"></a>
<FONT color="green">363</FONT>         * with no alteration.<a name="line.363"></a>
<FONT color="green">364</FONT>         *<a name="line.364"></a>
<FONT color="green">365</FONT>         * @param nreq how many of the regressors to include (either in canonical<a name="line.365"></a>
<FONT color="green">366</FONT>         * order, or in the current reordered state)<a name="line.366"></a>
<FONT color="green">367</FONT>         * @return an array with the estimated slope coefficients<a name="line.367"></a>
<FONT color="green">368</FONT>         * @throws ModelSpecificationException if {@code nreq} is less than 1<a name="line.368"></a>
<FONT color="green">369</FONT>         * or greater than the number of independent variables<a name="line.369"></a>
<FONT color="green">370</FONT>         */<a name="line.370"></a>
<FONT color="green">371</FONT>        private double[] regcf(int nreq) throws ModelSpecificationException {<a name="line.371"></a>
<FONT color="green">372</FONT>            int nextr;<a name="line.372"></a>
<FONT color="green">373</FONT>            if (nreq &lt; 1) {<a name="line.373"></a>
<FONT color="green">374</FONT>                throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);<a name="line.374"></a>
<FONT color="green">375</FONT>            }<a name="line.375"></a>
<FONT color="green">376</FONT>            if (nreq &gt; this.nvars) {<a name="line.376"></a>
<FONT color="green">377</FONT>                throw new ModelSpecificationException(<a name="line.377"></a>
<FONT color="green">378</FONT>                        LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);<a name="line.378"></a>
<FONT color="green">379</FONT>            }<a name="line.379"></a>
<FONT color="green">380</FONT>            if (!this.tol_set) {<a name="line.380"></a>
<FONT color="green">381</FONT>                tolset();<a name="line.381"></a>
<FONT color="green">382</FONT>            }<a name="line.382"></a>
<FONT color="green">383</FONT>            final double[] ret = new double[nreq];<a name="line.383"></a>
<FONT color="green">384</FONT>            boolean rankProblem = false;<a name="line.384"></a>
<FONT color="green">385</FONT>            for (int i = nreq - 1; i &gt; -1; i--) {<a name="line.385"></a>
<FONT color="green">386</FONT>                if (Math.sqrt(d[i]) &lt; tol[i]) {<a name="line.386"></a>
<FONT color="green">387</FONT>                    ret[i] = 0.0;<a name="line.387"></a>
<FONT color="green">388</FONT>                    d[i] = 0.0;<a name="line.388"></a>
<FONT color="green">389</FONT>                    rankProblem = true;<a name="line.389"></a>
<FONT color="green">390</FONT>                } else {<a name="line.390"></a>
<FONT color="green">391</FONT>                    ret[i] = rhs[i];<a name="line.391"></a>
<FONT color="green">392</FONT>                    nextr = i * (nvars + nvars - i - 1) / 2;<a name="line.392"></a>
<FONT color="green">393</FONT>                    for (int j = i + 1; j &lt; nreq; j++) {<a name="line.393"></a>
<FONT color="green">394</FONT>                        ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);<a name="line.394"></a>
<FONT color="green">395</FONT>                        ++nextr;<a name="line.395"></a>
<FONT color="green">396</FONT>                    }<a name="line.396"></a>
<FONT color="green">397</FONT>                }<a name="line.397"></a>
<FONT color="green">398</FONT>            }<a name="line.398"></a>
<FONT color="green">399</FONT>            if (rankProblem) {<a name="line.399"></a>
<FONT color="green">400</FONT>                for (int i = 0; i &lt; nreq; i++) {<a name="line.400"></a>
<FONT color="green">401</FONT>                    if (this.lindep[i]) {<a name="line.401"></a>
<FONT color="green">402</FONT>                        ret[i] = Double.NaN;<a name="line.402"></a>
<FONT color="green">403</FONT>                    }<a name="line.403"></a>
<FONT color="green">404</FONT>                }<a name="line.404"></a>
<FONT color="green">405</FONT>            }<a name="line.405"></a>
<FONT color="green">406</FONT>            return ret;<a name="line.406"></a>
<FONT color="green">407</FONT>        }<a name="line.407"></a>
<FONT color="green">408</FONT>    <a name="line.408"></a>
<FONT color="green">409</FONT>        /**<a name="line.409"></a>
<FONT color="green">410</FONT>         * The method which checks for singularities and then eliminates the offending<a name="line.410"></a>
<FONT color="green">411</FONT>         * columns.<a name="line.411"></a>
<FONT color="green">412</FONT>         */<a name="line.412"></a>
<FONT color="green">413</FONT>        private void singcheck() {<a name="line.413"></a>
<FONT color="green">414</FONT>            int pos;<a name="line.414"></a>
<FONT color="green">415</FONT>            for (int i = 0; i &lt; nvars; i++) {<a name="line.415"></a>
<FONT color="green">416</FONT>                work_sing[i] = Math.sqrt(d[i]);<a name="line.416"></a>
<FONT color="green">417</FONT>            }<a name="line.417"></a>
<FONT color="green">418</FONT>            for (int col = 0; col &lt; nvars; col++) {<a name="line.418"></a>
<FONT color="green">419</FONT>                // Set elements within R to zero if they are less than tol(col) in<a name="line.419"></a>
<FONT color="green">420</FONT>                // absolute value after being scaled by the square root of their row<a name="line.420"></a>
<FONT color="green">421</FONT>                // multiplier<a name="line.421"></a>
<FONT color="green">422</FONT>                final double temp = tol[col];<a name="line.422"></a>
<FONT color="green">423</FONT>                pos = col - 1;<a name="line.423"></a>
<FONT color="green">424</FONT>                for (int row = 0; row &lt; col - 1; row++) {<a name="line.424"></a>
<FONT color="green">425</FONT>                    if (Math.abs(r[pos]) * work_sing[row] &lt; temp) {<a name="line.425"></a>
<FONT color="green">426</FONT>                        r[pos] = 0.0;<a name="line.426"></a>
<FONT color="green">427</FONT>                    }<a name="line.427"></a>
<FONT color="green">428</FONT>                    pos += nvars - row - 2;<a name="line.428"></a>
<FONT color="green">429</FONT>                }<a name="line.429"></a>
<FONT color="green">430</FONT>                // If diagonal element is near zero, set it to zero, set appropriate<a name="line.430"></a>
<FONT color="green">431</FONT>                // element of LINDEP, and use INCLUD to augment the projections in<a name="line.431"></a>
<FONT color="green">432</FONT>                // the lower rows of the orthogonalization.<a name="line.432"></a>
<FONT color="green">433</FONT>                lindep[col] = false;<a name="line.433"></a>
<FONT color="green">434</FONT>                if (work_sing[col] &lt; temp) {<a name="line.434"></a>
<FONT color="green">435</FONT>                    lindep[col] = true;<a name="line.435"></a>
<FONT color="green">436</FONT>                    if (col &lt; nvars - 1) {<a name="line.436"></a>
<FONT color="green">437</FONT>                        Arrays.fill(x_sing, 0.0);<a name="line.437"></a>
<FONT color="green">438</FONT>                        int _pi = col * (nvars + nvars - col - 1) / 2;<a name="line.438"></a>
<FONT color="green">439</FONT>                        for (int _xi = col + 1; _xi &lt; nvars; _xi++, _pi++) {<a name="line.439"></a>
<FONT color="green">440</FONT>                            x_sing[_xi] = r[_pi];<a name="line.440"></a>
<FONT color="green">441</FONT>                            r[_pi] = 0.0;<a name="line.441"></a>
<FONT color="green">442</FONT>                        }<a name="line.442"></a>
<FONT color="green">443</FONT>                        final double y = rhs[col];<a name="line.443"></a>
<FONT color="green">444</FONT>                        final double weight = d[col];<a name="line.444"></a>
<FONT color="green">445</FONT>                        d[col] = 0.0;<a name="line.445"></a>
<FONT color="green">446</FONT>                        rhs[col] = 0.0;<a name="line.446"></a>
<FONT color="green">447</FONT>                        this.include(x_sing, weight, y);<a name="line.447"></a>
<FONT color="green">448</FONT>                    } else {<a name="line.448"></a>
<FONT color="green">449</FONT>                        sserr += d[col] * rhs[col] * rhs[col];<a name="line.449"></a>
<FONT color="green">450</FONT>                    }<a name="line.450"></a>
<FONT color="green">451</FONT>                }<a name="line.451"></a>
<FONT color="green">452</FONT>            }<a name="line.452"></a>
<FONT color="green">453</FONT>        }<a name="line.453"></a>
<FONT color="green">454</FONT>    <a name="line.454"></a>
<FONT color="green">455</FONT>        /**<a name="line.455"></a>
<FONT color="green">456</FONT>         * Calculates the sum of squared errors for the full regression<a name="line.456"></a>
<FONT color="green">457</FONT>         * and all subsets in the following manner: &lt;pre&gt;<a name="line.457"></a>
<FONT color="green">458</FONT>         * rss[] ={<a name="line.458"></a>
<FONT color="green">459</FONT>         * ResidualSumOfSquares_allNvars,<a name="line.459"></a>
<FONT color="green">460</FONT>         * ResidualSumOfSquares_FirstNvars-1,<a name="line.460"></a>
<FONT color="green">461</FONT>         * ResidualSumOfSquares_FirstNvars-2,<a name="line.461"></a>
<FONT color="green">462</FONT>         * ..., ResidualSumOfSquares_FirstVariable} &lt;/pre&gt;<a name="line.462"></a>
<FONT color="green">463</FONT>         */<a name="line.463"></a>
<FONT color="green">464</FONT>        private void ss() {<a name="line.464"></a>
<FONT color="green">465</FONT>            double total = sserr;<a name="line.465"></a>
<FONT color="green">466</FONT>            rss[nvars - 1] = sserr;<a name="line.466"></a>
<FONT color="green">467</FONT>            for (int i = nvars - 1; i &gt; 0; i--) {<a name="line.467"></a>
<FONT color="green">468</FONT>                total += d[i] * rhs[i] * rhs[i];<a name="line.468"></a>
<FONT color="green">469</FONT>                rss[i - 1] = total;<a name="line.469"></a>
<FONT color="green">470</FONT>            }<a name="line.470"></a>
<FONT color="green">471</FONT>            rss_set = true;<a name="line.471"></a>
<FONT color="green">472</FONT>        }<a name="line.472"></a>
<FONT color="green">473</FONT>    <a name="line.473"></a>
<FONT color="green">474</FONT>        /**<a name="line.474"></a>
<FONT color="green">475</FONT>         * Calculates the cov matrix assuming only the first nreq variables are<a name="line.475"></a>
<FONT color="green">476</FONT>         * included in the calculation. The returned array contains a symmetric<a name="line.476"></a>
<FONT color="green">477</FONT>         * matrix stored in lower triangular form. The matrix will have<a name="line.477"></a>
<FONT color="green">478</FONT>         * ( nreq + 1 ) * nreq / 2 elements. For illustration &lt;pre&gt;<a name="line.478"></a>
<FONT color="green">479</FONT>         * cov =<a name="line.479"></a>
<FONT color="green">480</FONT>         * {<a name="line.480"></a>
<FONT color="green">481</FONT>         *  cov_00,<a name="line.481"></a>
<FONT color="green">482</FONT>         *  cov_10, cov_11,<a name="line.482"></a>
<FONT color="green">483</FONT>         *  cov_20, cov_21, cov22,<a name="line.483"></a>
<FONT color="green">484</FONT>         *  ...<a name="line.484"></a>
<FONT color="green">485</FONT>         * } &lt;/pre&gt;<a name="line.485"></a>
<FONT color="green">486</FONT>         *<a name="line.486"></a>
<FONT color="green">487</FONT>         * @param nreq how many of the regressors to include (either in canonical<a name="line.487"></a>
<FONT color="green">488</FONT>         * order, or in the current reordered state)<a name="line.488"></a>
<FONT color="green">489</FONT>         * @return an array with the variance covariance of the included<a name="line.489"></a>
<FONT color="green">490</FONT>         * regressors in lower triangular form<a name="line.490"></a>
<FONT color="green">491</FONT>         */<a name="line.491"></a>
<FONT color="green">492</FONT>        private double[] cov(int nreq) {<a name="line.492"></a>
<FONT color="green">493</FONT>            if (this.nobs &lt;= nreq) {<a name="line.493"></a>
<FONT color="green">494</FONT>                return null;<a name="line.494"></a>
<FONT color="green">495</FONT>            }<a name="line.495"></a>
<FONT color="green">496</FONT>            double rnk = 0.0;<a name="line.496"></a>
<FONT color="green">497</FONT>            for (int i = 0; i &lt; nreq; i++) {<a name="line.497"></a>
<FONT color="green">498</FONT>                if (!this.lindep[i]) {<a name="line.498"></a>
<FONT color="green">499</FONT>                    rnk += 1.0;<a name="line.499"></a>
<FONT color="green">500</FONT>                }<a name="line.500"></a>
<FONT color="green">501</FONT>            }<a name="line.501"></a>
<FONT color="green">502</FONT>            final double var = rss[nreq - 1] / (nobs - rnk);<a name="line.502"></a>
<FONT color="green">503</FONT>            final double[] rinv = new double[nreq * (nreq - 1) / 2];<a name="line.503"></a>
<FONT color="green">504</FONT>            inverse(rinv, nreq);<a name="line.504"></a>
<FONT color="green">505</FONT>            final double[] covmat = new double[nreq * (nreq + 1) / 2];<a name="line.505"></a>
<FONT color="green">506</FONT>            Arrays.fill(covmat, Double.NaN);<a name="line.506"></a>
<FONT color="green">507</FONT>            int pos2;<a name="line.507"></a>
<FONT color="green">508</FONT>            int pos1;<a name="line.508"></a>
<FONT color="green">509</FONT>            int start = 0;<a name="line.509"></a>
<FONT color="green">510</FONT>            double total = 0;<a name="line.510"></a>
<FONT color="green">511</FONT>            for (int row = 0; row &lt; nreq; row++) {<a name="line.511"></a>
<FONT color="green">512</FONT>                pos2 = start;<a name="line.512"></a>
<FONT color="green">513</FONT>                if (!this.lindep[row]) {<a name="line.513"></a>
<FONT color="green">514</FONT>                    for (int col = row; col &lt; nreq; col++) {<a name="line.514"></a>
<FONT color="green">515</FONT>                        if (!this.lindep[col]) {<a name="line.515"></a>
<FONT color="green">516</FONT>                            pos1 = start + col - row;<a name="line.516"></a>
<FONT color="green">517</FONT>                            if (row == col) {<a name="line.517"></a>
<FONT color="green">518</FONT>                                total = 1.0 / d[col];<a name="line.518"></a>
<FONT color="green">519</FONT>                            } else {<a name="line.519"></a>
<FONT color="green">520</FONT>                                total = rinv[pos1 - 1] / d[col];<a name="line.520"></a>
<FONT color="green">521</FONT>                            }<a name="line.521"></a>
<FONT color="green">522</FONT>                            for (int k = col + 1; k &lt; nreq; k++) {<a name="line.522"></a>
<FONT color="green">523</FONT>                                if (!this.lindep[k]) {<a name="line.523"></a>
<FONT color="green">524</FONT>                                    total += rinv[pos1] * rinv[pos2] / d[k];<a name="line.524"></a>
<FONT color="green">525</FONT>                                }<a name="line.525"></a>
<FONT color="green">526</FONT>                                ++pos1;<a name="line.526"></a>
<FONT color="green">527</FONT>                                ++pos2;<a name="line.527"></a>
<FONT color="green">528</FONT>                            }<a name="line.528"></a>
<FONT color="green">529</FONT>                            covmat[ (col + 1) * col / 2 + row] = total * var;<a name="line.529"></a>
<FONT color="green">530</FONT>                        } else {<a name="line.530"></a>
<FONT color="green">531</FONT>                            pos2 += nreq - col - 1;<a name="line.531"></a>
<FONT color="green">532</FONT>                        }<a name="line.532"></a>
<FONT color="green">533</FONT>                    }<a name="line.533"></a>
<FONT color="green">534</FONT>                }<a name="line.534"></a>
<FONT color="green">535</FONT>                start += nreq - row - 1;<a name="line.535"></a>
<FONT color="green">536</FONT>            }<a name="line.536"></a>
<FONT color="green">537</FONT>            return covmat;<a name="line.537"></a>
<FONT color="green">538</FONT>        }<a name="line.538"></a>
<FONT color="green">539</FONT>    <a name="line.539"></a>
<FONT color="green">540</FONT>        /**<a name="line.540"></a>
<FONT color="green">541</FONT>         * This internal method calculates the inverse of the upper-triangular portion<a name="line.541"></a>
<FONT color="green">542</FONT>         * of the R matrix.<a name="line.542"></a>
<FONT color="green">543</FONT>         * @param rinv  the storage for the inverse of r<a name="line.543"></a>
<FONT color="green">544</FONT>         * @param nreq how many of the regressors to include (either in canonical<a name="line.544"></a>
<FONT color="green">545</FONT>         * order, or in the current reordered state)<a name="line.545"></a>
<FONT color="green">546</FONT>         */<a name="line.546"></a>
<FONT color="green">547</FONT>        private void inverse(double[] rinv, int nreq) {<a name="line.547"></a>
<FONT color="green">548</FONT>            int pos = nreq * (nreq - 1) / 2 - 1;<a name="line.548"></a>
<FONT color="green">549</FONT>            int pos1 = -1;<a name="line.549"></a>
<FONT color="green">550</FONT>            int pos2 = -1;<a name="line.550"></a>
<FONT color="green">551</FONT>            double total = 0.0;<a name="line.551"></a>
<FONT color="green">552</FONT>            Arrays.fill(rinv, Double.NaN);<a name="line.552"></a>
<FONT color="green">553</FONT>            for (int row = nreq - 1; row &gt; 0; --row) {<a name="line.553"></a>
<FONT color="green">554</FONT>                if (!this.lindep[row]) {<a name="line.554"></a>
<FONT color="green">555</FONT>                    final int start = (row - 1) * (nvars + nvars - row) / 2;<a name="line.555"></a>
<FONT color="green">556</FONT>                    for (int col = nreq; col &gt; row; --col) {<a name="line.556"></a>
<FONT color="green">557</FONT>                        pos1 = start;<a name="line.557"></a>
<FONT color="green">558</FONT>                        pos2 = pos;<a name="line.558"></a>
<FONT color="green">559</FONT>                        total = 0.0;<a name="line.559"></a>
<FONT color="green">560</FONT>                        for (int k = row; k &lt; col - 1; k++) {<a name="line.560"></a>
<FONT color="green">561</FONT>                            pos2 += nreq - k - 1;<a name="line.561"></a>
<FONT color="green">562</FONT>                            if (!this.lindep[k]) {<a name="line.562"></a>
<FONT color="green">563</FONT>                                total += -r[pos1] * rinv[pos2];<a name="line.563"></a>
<FONT color="green">564</FONT>                            }<a name="line.564"></a>
<FONT color="green">565</FONT>                            ++pos1;<a name="line.565"></a>
<FONT color="green">566</FONT>                        }<a name="line.566"></a>
<FONT color="green">567</FONT>                        rinv[pos] = total - r[pos1];<a name="line.567"></a>
<FONT color="green">568</FONT>                        --pos;<a name="line.568"></a>
<FONT color="green">569</FONT>                    }<a name="line.569"></a>
<FONT color="green">570</FONT>                } else {<a name="line.570"></a>
<FONT color="green">571</FONT>                    pos -= nreq - row;<a name="line.571"></a>
<FONT color="green">572</FONT>                }<a name="line.572"></a>
<FONT color="green">573</FONT>            }<a name="line.573"></a>
<FONT color="green">574</FONT>        }<a name="line.574"></a>
<FONT color="green">575</FONT>    <a name="line.575"></a>
<FONT color="green">576</FONT>        /**<a name="line.576"></a>
<FONT color="green">577</FONT>         * In the original algorithm only the partial correlations of the regressors<a name="line.577"></a>
<FONT color="green">578</FONT>         * is returned to the user. In this implementation, we have &lt;pre&gt;<a name="line.578"></a>
<FONT color="green">579</FONT>         * corr =<a name="line.579"></a>
<FONT color="green">580</FONT>         * {<a name="line.580"></a>
<FONT color="green">581</FONT>         *   corrxx - lower triangular<a name="line.581"></a>
<FONT color="green">582</FONT>         *   corrxy - bottom row of the matrix<a name="line.582"></a>
<FONT color="green">583</FONT>         * }<a name="line.583"></a>
<FONT color="green">584</FONT>         * Replaces subroutines PCORR and COR of:<a name="line.584"></a>
<FONT color="green">585</FONT>         * ALGORITHM AS274  APPL. STATIST. (1992) VOL.41, NO. 2 &lt;/pre&gt;<a name="line.585"></a>
<FONT color="green">586</FONT>         *<a name="line.586"></a>
<FONT color="green">587</FONT>         * &lt;p&gt;Calculate partial correlations after the variables in rows<a name="line.587"></a>
<FONT color="green">588</FONT>         * 1, 2, ..., IN have been forced into the regression.<a name="line.588"></a>
<FONT color="green">589</FONT>         * If IN = 1, and the first row of R represents a constant in the<a name="line.589"></a>
<FONT color="green">590</FONT>         * model, then the usual simple correlations are returned.&lt;/p&gt;<a name="line.590"></a>
<FONT color="green">591</FONT>         *<a name="line.591"></a>
<FONT color="green">592</FONT>         * &lt;p&gt;If IN = 0, the value returned in array CORMAT for the correlation<a name="line.592"></a>
<FONT color="green">593</FONT>         * of variables Xi &amp; Xj is: &lt;pre&gt;<a name="line.593"></a>
<FONT color="green">594</FONT>         * sum ( Xi.Xj ) / Sqrt ( sum (Xi^2) . sum (Xj^2) )&lt;/pre&gt;&lt;/p&gt;<a name="line.594"></a>
<FONT color="green">595</FONT>         *<a name="line.595"></a>
<FONT color="green">596</FONT>         * &lt;p&gt;On return, array CORMAT contains the upper triangle of the matrix of<a name="line.596"></a>
<FONT color="green">597</FONT>         * partial correlations stored by rows, excluding the 1's on the diagonal.<a name="line.597"></a>
<FONT color="green">598</FONT>         * e.g. if IN = 2, the consecutive elements returned are:<a name="line.598"></a>
<FONT color="green">599</FONT>         * (3,4) (3,5) ... (3,ncol), (4,5) (4,6) ... (4,ncol), etc.<a name="line.599"></a>
<FONT color="green">600</FONT>         * Array YCORR stores the partial correlations with the Y-variable<a name="line.600"></a>
<FONT color="green">601</FONT>         * starting with YCORR(IN+1) = partial correlation with the variable in<a name="line.601"></a>
<FONT color="green">602</FONT>         * position (IN+1). &lt;/p&gt;<a name="line.602"></a>
<FONT color="green">603</FONT>         *<a name="line.603"></a>
<FONT color="green">604</FONT>         * @param in how many of the regressors to include (either in canonical<a name="line.604"></a>
<FONT color="green">605</FONT>         * order, or in the current reordered state)<a name="line.605"></a>
<FONT color="green">606</FONT>         * @return an array with the partial correlations of the remainder of<a name="line.606"></a>
<FONT color="green">607</FONT>         * regressors with each other and the regressand, in lower triangular form<a name="line.607"></a>
<FONT color="green">608</FONT>         */<a name="line.608"></a>
<FONT color="green">609</FONT>        public double[] getPartialCorrelations(int in) {<a name="line.609"></a>
<FONT color="green">610</FONT>            final double[] output = new double[(nvars - in + 1) * (nvars - in) / 2];<a name="line.610"></a>
<FONT color="green">611</FONT>            int pos;<a name="line.611"></a>
<FONT color="green">612</FONT>            int pos1;<a name="line.612"></a>
<FONT color="green">613</FONT>            int pos2;<a name="line.613"></a>
<FONT color="green">614</FONT>            final int rms_off = -in;<a name="line.614"></a>
<FONT color="green">615</FONT>            final int wrk_off = -(in + 1);<a name="line.615"></a>
<FONT color="green">616</FONT>            final double[] rms = new double[nvars - in];<a name="line.616"></a>
<FONT color="green">617</FONT>            final double[] work = new double[nvars - in - 1];<a name="line.617"></a>
<FONT color="green">618</FONT>            double sumxx;<a name="line.618"></a>
<FONT color="green">619</FONT>            double sumxy;<a name="line.619"></a>
<FONT color="green">620</FONT>            double sumyy;<a name="line.620"></a>
<FONT color="green">621</FONT>            final int offXX = (nvars - in) * (nvars - in - 1) / 2;<a name="line.621"></a>
<FONT color="green">622</FONT>            if (in &lt; -1 || in &gt;= nvars) {<a name="line.622"></a>
<FONT color="green">623</FONT>                return null;<a name="line.623"></a>
<FONT color="green">624</FONT>            }<a name="line.624"></a>
<FONT color="green">625</FONT>            final int nvm = nvars - 1;<a name="line.625"></a>
<FONT color="green">626</FONT>            final int base_pos = r.length - (nvm - in) * (nvm - in + 1) / 2;<a name="line.626"></a>
<FONT color="green">627</FONT>            if (d[in] &gt; 0.0) {<a name="line.627"></a>
<FONT color="green">628</FONT>                rms[in + rms_off] = 1.0 / Math.sqrt(d[in]);<a name="line.628"></a>
<FONT color="green">629</FONT>            }<a name="line.629"></a>
<FONT color="green">630</FONT>            for (int col = in + 1; col &lt; nvars; col++) {<a name="line.630"></a>
<FONT color="green">631</FONT>                pos = base_pos + col - 1 - in;<a name="line.631"></a>
<FONT color="green">632</FONT>                sumxx = d[col];<a name="line.632"></a>
<FONT color="green">633</FONT>                for (int row = in; row &lt; col; row++) {<a name="line.633"></a>
<FONT color="green">634</FONT>                    sumxx += d[row] * r[pos] * r[pos];<a name="line.634"></a>
<FONT color="green">635</FONT>                    pos += nvars - row - 2;<a name="line.635"></a>
<FONT color="green">636</FONT>                }<a name="line.636"></a>
<FONT color="green">637</FONT>                if (sumxx &gt; 0.0) {<a name="line.637"></a>
<FONT color="green">638</FONT>                    rms[col + rms_off] = 1.0 / Math.sqrt(sumxx);<a name="line.638"></a>
<FONT color="green">639</FONT>                } else {<a name="line.639"></a>
<FONT color="green">640</FONT>                    rms[col + rms_off] = 0.0;<a name="line.640"></a>
<FONT color="green">641</FONT>                }<a name="line.641"></a>
<FONT color="green">642</FONT>            }<a name="line.642"></a>
<FONT color="green">643</FONT>            sumyy = sserr;<a name="line.643"></a>
<FONT color="green">644</FONT>            for (int row = in; row &lt; nvars; row++) {<a name="line.644"></a>
<FONT color="green">645</FONT>                sumyy += d[row] * rhs[row] * rhs[row];<a name="line.645"></a>
<FONT color="green">646</FONT>            }<a name="line.646"></a>
<FONT color="green">647</FONT>            if (sumyy &gt; 0.0) {<a name="line.647"></a>
<FONT color="green">648</FONT>                sumyy = 1.0 / Math.sqrt(sumyy);<a name="line.648"></a>
<FONT color="green">649</FONT>            }<a name="line.649"></a>
<FONT color="green">650</FONT>            pos = 0;<a name="line.650"></a>
<FONT color="green">651</FONT>            for (int col1 = in; col1 &lt; nvars; col1++) {<a name="line.651"></a>
<FONT color="green">652</FONT>                sumxy = 0.0;<a name="line.652"></a>
<FONT color="green">653</FONT>                Arrays.fill(work, 0.0);<a name="line.653"></a>
<FONT color="green">654</FONT>                pos1 = base_pos + col1 - in - 1;<a name="line.654"></a>
<FONT color="green">655</FONT>                for (int row = in; row &lt; col1; row++) {<a name="line.655"></a>
<FONT color="green">656</FONT>                    pos2 = pos1 + 1;<a name="line.656"></a>
<FONT color="green">657</FONT>                    for (int col2 = col1 + 1; col2 &lt; nvars; col2++) {<a name="line.657"></a>
<FONT color="green">658</FONT>                        work[col2 + wrk_off] += d[row] * r[pos1] * r[pos2];<a name="line.658"></a>
<FONT color="green">659</FONT>                        pos2++;<a name="line.659"></a>
<FONT color="green">660</FONT>                    }<a name="line.660"></a>
<FONT color="green">661</FONT>                    sumxy += d[row] * r[pos1] * rhs[row];<a name="line.661"></a>
<FONT color="green">662</FONT>                    pos1 += nvars - row - 2;<a name="line.662"></a>
<FONT color="green">663</FONT>                }<a name="line.663"></a>
<FONT color="green">664</FONT>                pos2 = pos1 + 1;<a name="line.664"></a>
<FONT color="green">665</FONT>                for (int col2 = col1 + 1; col2 &lt; nvars; col2++) {<a name="line.665"></a>
<FONT color="green">666</FONT>                    work[col2 + wrk_off] += d[col1] * r[pos2];<a name="line.666"></a>
<FONT color="green">667</FONT>                    ++pos2;<a name="line.667"></a>
<FONT color="green">668</FONT>                    output[ (col2 - 1 - in) * (col2 - in) / 2 + col1 - in] =<a name="line.668"></a>
<FONT color="green">669</FONT>                            work[col2 + wrk_off] * rms[col1 + rms_off] * rms[col2 + rms_off];<a name="line.669"></a>
<FONT color="green">670</FONT>                    ++pos;<a name="line.670"></a>
<FONT color="green">671</FONT>                }<a name="line.671"></a>
<FONT color="green">672</FONT>                sumxy += d[col1] * rhs[col1];<a name="line.672"></a>
<FONT color="green">673</FONT>                output[col1 + rms_off + offXX] = sumxy * rms[col1 + rms_off] * sumyy;<a name="line.673"></a>
<FONT color="green">674</FONT>            }<a name="line.674"></a>
<FONT color="green">675</FONT>    <a name="line.675"></a>
<FONT color="green">676</FONT>            return output;<a name="line.676"></a>
<FONT color="green">677</FONT>        }<a name="line.677"></a>
<FONT color="green">678</FONT>    <a name="line.678"></a>
<FONT color="green">679</FONT>        /**<a name="line.679"></a>
<FONT color="green">680</FONT>         * ALGORITHM AS274 APPL. STATIST. (1992) VOL.41, NO. 2.<a name="line.680"></a>
<FONT color="green">681</FONT>         * Move variable from position FROM to position TO in an<a name="line.681"></a>
<FONT color="green">682</FONT>         * orthogonal reduction produced by AS75.1.<a name="line.682"></a>
<FONT color="green">683</FONT>         *<a name="line.683"></a>
<FONT color="green">684</FONT>         * @param from initial position<a name="line.684"></a>
<FONT color="green">685</FONT>         * @param to destination<a name="line.685"></a>
<FONT color="green">686</FONT>         */<a name="line.686"></a>
<FONT color="green">687</FONT>        private void vmove(int from, int to) {<a name="line.687"></a>
<FONT color="green">688</FONT>            double d1;<a name="line.688"></a>
<FONT color="green">689</FONT>            double d2;<a name="line.689"></a>
<FONT color="green">690</FONT>            double X;<a name="line.690"></a>
<FONT color="green">691</FONT>            double d1new;<a name="line.691"></a>
<FONT color="green">692</FONT>            double d2new;<a name="line.692"></a>
<FONT color="green">693</FONT>            double cbar;<a name="line.693"></a>
<FONT color="green">694</FONT>            double sbar;<a name="line.694"></a>
<FONT color="green">695</FONT>            double Y;<a name="line.695"></a>
<FONT color="green">696</FONT>            int first;<a name="line.696"></a>
<FONT color="green">697</FONT>            int inc;<a name="line.697"></a>
<FONT color="green">698</FONT>            int m1;<a name="line.698"></a>
<FONT color="green">699</FONT>            int m2;<a name="line.699"></a>
<FONT color="green">700</FONT>            int mp1;<a name="line.700"></a>
<FONT color="green">701</FONT>            int pos;<a name="line.701"></a>
<FONT color="green">702</FONT>            boolean bSkipTo40 = false;<a name="line.702"></a>
<FONT color="green">703</FONT>            if (from == to) {<a name="line.703"></a>
<FONT color="green">704</FONT>                return;<a name="line.704"></a>
<FONT color="green">705</FONT>            }<a name="line.705"></a>
<FONT color="green">706</FONT>            if (!this.rss_set) {<a name="line.706"></a>
<FONT color="green">707</FONT>                ss();<a name="line.707"></a>
<FONT color="green">708</FONT>            }<a name="line.708"></a>
<FONT color="green">709</FONT>            int count = 0;<a name="line.709"></a>
<FONT color="green">710</FONT>            if (from &lt; to) {<a name="line.710"></a>
<FONT color="green">711</FONT>                first = from;<a name="line.711"></a>
<FONT color="green">712</FONT>                inc = 1;<a name="line.712"></a>
<FONT color="green">713</FONT>                count = to - from;<a name="line.713"></a>
<FONT color="green">714</FONT>            } else {<a name="line.714"></a>
<FONT color="green">715</FONT>                first = from - 1;<a name="line.715"></a>
<FONT color="green">716</FONT>                inc = -1;<a name="line.716"></a>
<FONT color="green">717</FONT>                count = from - to;<a name="line.717"></a>
<FONT color="green">718</FONT>            }<a name="line.718"></a>
<FONT color="green">719</FONT>    <a name="line.719"></a>
<FONT color="green">720</FONT>            int m = first;<a name="line.720"></a>
<FONT color="green">721</FONT>            int idx = 0;<a name="line.721"></a>
<FONT color="green">722</FONT>            while (idx &lt; count) {<a name="line.722"></a>
<FONT color="green">723</FONT>                m1 = m * (nvars + nvars - m - 1) / 2;<a name="line.723"></a>
<FONT color="green">724</FONT>                m2 = m1 + nvars - m - 1;<a name="line.724"></a>
<FONT color="green">725</FONT>                mp1 = m + 1;<a name="line.725"></a>
<FONT color="green">726</FONT>    <a name="line.726"></a>
<FONT color="green">727</FONT>                d1 = d[m];<a name="line.727"></a>
<FONT color="green">728</FONT>                d2 = d[mp1];<a name="line.728"></a>
<FONT color="green">729</FONT>                // Special cases.<a name="line.729"></a>
<FONT color="green">730</FONT>                if (d1 &gt; this.epsilon || d2 &gt; this.epsilon) {<a name="line.730"></a>
<FONT color="green">731</FONT>                    X = r[m1];<a name="line.731"></a>
<FONT color="green">732</FONT>                    if (Math.abs(X) * Math.sqrt(d1) &lt; tol[mp1]) {<a name="line.732"></a>
<FONT color="green">733</FONT>                        X = 0.0;<a name="line.733"></a>
<FONT color="green">734</FONT>                    }<a name="line.734"></a>
<FONT color="green">735</FONT>                    if (d1 &lt; this.epsilon || Math.abs(X) &lt; this.epsilon) {<a name="line.735"></a>
<FONT color="green">736</FONT>                        d[m] = d2;<a name="line.736"></a>
<FONT color="green">737</FONT>                        d[mp1] = d1;<a name="line.737"></a>
<FONT color="green">738</FONT>                        r[m1] = 0.0;<a name="line.738"></a>
<FONT color="green">739</FONT>                        for (int col = m + 2; col &lt; nvars; col++) {<a name="line.739"></a>
<FONT color="green">740</FONT>                            ++m1;<a name="line.740"></a>
<FONT color="green">741</FONT>                            X = r[m1];<a name="line.741"></a>
<FONT color="green">742</FONT>                            r[m1] = r[m2];<a name="line.742"></a>
<FONT color="green">743</FONT>                            r[m2] = X;<a name="line.743"></a>
<FONT color="green">744</FONT>                            ++m2;<a name="line.744"></a>
<FONT color="green">745</FONT>                        }<a name="line.745"></a>
<FONT color="green">746</FONT>                        X = rhs[m];<a name="line.746"></a>
<FONT color="green">747</FONT>                        rhs[m] = rhs[mp1];<a name="line.747"></a>
<FONT color="green">748</FONT>                        rhs[mp1] = X;<a name="line.748"></a>
<FONT color="green">749</FONT>                        bSkipTo40 = true;<a name="line.749"></a>
<FONT color="green">750</FONT>                        //break;<a name="line.750"></a>
<FONT color="green">751</FONT>                    } else if (d2 &lt; this.epsilon) {<a name="line.751"></a>
<FONT color="green">752</FONT>                        d[m] = d1 * X * X;<a name="line.752"></a>
<FONT color="green">753</FONT>                        r[m1] = 1.0 / X;<a name="line.753"></a>
<FONT color="green">754</FONT>                        for (int _i = m1 + 1; _i &lt; m1 + nvars - m - 1; _i++) {<a name="line.754"></a>
<FONT color="green">755</FONT>                            r[_i] /= X;<a name="line.755"></a>
<FONT color="green">756</FONT>                        }<a name="line.756"></a>
<FONT color="green">757</FONT>                        rhs[m] = rhs[m] / X;<a name="line.757"></a>
<FONT color="green">758</FONT>                        bSkipTo40 = true;<a name="line.758"></a>
<FONT color="green">759</FONT>                        //break;<a name="line.759"></a>
<FONT color="green">760</FONT>                    }<a name="line.760"></a>
<FONT color="green">761</FONT>                    if (!bSkipTo40) {<a name="line.761"></a>
<FONT color="green">762</FONT>                        d1new = d2 + d1 * X * X;<a name="line.762"></a>
<FONT color="green">763</FONT>                        cbar = d2 / d1new;<a name="line.763"></a>
<FONT color="green">764</FONT>                        sbar = X * d1 / d1new;<a name="line.764"></a>
<FONT color="green">765</FONT>                        d2new = d1 * cbar;<a name="line.765"></a>
<FONT color="green">766</FONT>                        d[m] = d1new;<a name="line.766"></a>
<FONT color="green">767</FONT>                        d[mp1] = d2new;<a name="line.767"></a>
<FONT color="green">768</FONT>                        r[m1] = sbar;<a name="line.768"></a>
<FONT color="green">769</FONT>                        for (int col = m + 2; col &lt; nvars; col++) {<a name="line.769"></a>
<FONT color="green">770</FONT>                            ++m1;<a name="line.770"></a>
<FONT color="green">771</FONT>                            Y = r[m1];<a name="line.771"></a>
<FONT color="green">772</FONT>                            r[m1] = cbar * r[m2] + sbar * Y;<a name="line.772"></a>
<FONT color="green">773</FONT>                            r[m2] = Y - X * r[m2];<a name="line.773"></a>
<FONT color="green">774</FONT>                            ++m2;<a name="line.774"></a>
<FONT color="green">775</FONT>                        }<a name="line.775"></a>
<FONT color="green">776</FONT>                        Y = rhs[m];<a name="line.776"></a>
<FONT color="green">777</FONT>                        rhs[m] = cbar * rhs[mp1] + sbar * Y;<a name="line.777"></a>
<FONT color="green">778</FONT>                        rhs[mp1] = Y - X * rhs[mp1];<a name="line.778"></a>
<FONT color="green">779</FONT>                    }<a name="line.779"></a>
<FONT color="green">780</FONT>                }<a name="line.780"></a>
<FONT color="green">781</FONT>                if (m &gt; 0) {<a name="line.781"></a>
<FONT color="green">782</FONT>                    pos = m;<a name="line.782"></a>
<FONT color="green">783</FONT>                    for (int row = 0; row &lt; m; row++) {<a name="line.783"></a>
<FONT color="green">784</FONT>                        X = r[pos];<a name="line.784"></a>
<FONT color="green">785</FONT>                        r[pos] = r[pos - 1];<a name="line.785"></a>
<FONT color="green">786</FONT>                        r[pos - 1] = X;<a name="line.786"></a>
<FONT color="green">787</FONT>                        pos += nvars - row - 2;<a name="line.787"></a>
<FONT color="green">788</FONT>                    }<a name="line.788"></a>
<FONT color="green">789</FONT>                }<a name="line.789"></a>
<FONT color="green">790</FONT>                // Adjust variable order (VORDER), the tolerances (TOL) and<a name="line.790"></a>
<FONT color="green">791</FONT>                // the vector of residual sums of squares (RSS).<a name="line.791"></a>
<FONT color="green">792</FONT>                m1 = vorder[m];<a name="line.792"></a>
<FONT color="green">793</FONT>                vorder[m] = vorder[mp1];<a name="line.793"></a>
<FONT color="green">794</FONT>                vorder[mp1] = m1;<a name="line.794"></a>
<FONT color="green">795</FONT>                X = tol[m];<a name="line.795"></a>
<FONT color="green">796</FONT>                tol[m] = tol[mp1];<a name="line.796"></a>
<FONT color="green">797</FONT>                tol[mp1] = X;<a name="line.797"></a>
<FONT color="green">798</FONT>                rss[m] = rss[mp1] + d[mp1] * rhs[mp1] * rhs[mp1];<a name="line.798"></a>
<FONT color="green">799</FONT>    <a name="line.799"></a>
<FONT color="green">800</FONT>                m += inc;<a name="line.800"></a>
<FONT color="green">801</FONT>                ++idx;<a name="line.801"></a>
<FONT color="green">802</FONT>            }<a name="line.802"></a>
<FONT color="green">803</FONT>        }<a name="line.803"></a>
<FONT color="green">804</FONT>    <a name="line.804"></a>
<FONT color="green">805</FONT>        /**<a name="line.805"></a>
<FONT color="green">806</FONT>         * ALGORITHM AS274  APPL. STATIST. (1992) VOL.41, NO. 2<a name="line.806"></a>
<FONT color="green">807</FONT>         *<a name="line.807"></a>
<FONT color="green">808</FONT>         * &lt;p&gt; Re-order the variables in an orthogonal reduction produced by<a name="line.808"></a>
<FONT color="green">809</FONT>         * AS75.1 so that the N variables in LIST start at position POS1,<a name="line.809"></a>
<FONT color="green">810</FONT>         * though will not necessarily be in the same order as in LIST.<a name="line.810"></a>
<FONT color="green">811</FONT>         * Any variables in VORDER before position POS1 are not moved.<a name="line.811"></a>
<FONT color="green">812</FONT>         * Auxiliary routine called: VMOVE. &lt;/p&gt;<a name="line.812"></a>
<FONT color="green">813</FONT>         *<a name="line.813"></a>
<FONT color="green">814</FONT>         * &lt;p&gt;This internal method reorders the regressors.&lt;/p&gt;<a name="line.814"></a>
<FONT color="green">815</FONT>         *<a name="line.815"></a>
<FONT color="green">816</FONT>         * @param list the regressors to move<a name="line.816"></a>
<FONT color="green">817</FONT>         * @param pos1 where the list will be placed<a name="line.817"></a>
<FONT color="green">818</FONT>         * @return -1 error, 0 everything ok<a name="line.818"></a>
<FONT color="green">819</FONT>         */<a name="line.819"></a>
<FONT color="green">820</FONT>        private int reorderRegressors(int[] list, int pos1) {<a name="line.820"></a>
<FONT color="green">821</FONT>            int next;<a name="line.821"></a>
<FONT color="green">822</FONT>            int i;<a name="line.822"></a>
<FONT color="green">823</FONT>            int l;<a name="line.823"></a>
<FONT color="green">824</FONT>            if (list.length &lt; 1 || list.length &gt; nvars + 1 - pos1) {<a name="line.824"></a>
<FONT color="green">825</FONT>                return -1;<a name="line.825"></a>
<FONT color="green">826</FONT>            }<a name="line.826"></a>
<FONT color="green">827</FONT>            next = pos1;<a name="line.827"></a>
<FONT color="green">828</FONT>            i = pos1;<a name="line.828"></a>
<FONT color="green">829</FONT>            while (i &lt; nvars) {<a name="line.829"></a>
<FONT color="green">830</FONT>                l = vorder[i];<a name="line.830"></a>
<FONT color="green">831</FONT>                for (int j = 0; j &lt; list.length; j++) {<a name="line.831"></a>
<FONT color="green">832</FONT>                    if (l == list[j] &amp;&amp; i &gt; next) {<a name="line.832"></a>
<FONT color="green">833</FONT>                        this.vmove(i, next);<a name="line.833"></a>
<FONT color="green">834</FONT>                        ++next;<a name="line.834"></a>
<FONT color="green">835</FONT>                        if (next &gt;= list.length + pos1) {<a name="line.835"></a>
<FONT color="green">836</FONT>                            return 0;<a name="line.836"></a>
<FONT color="green">837</FONT>                        } else {<a name="line.837"></a>
<FONT color="green">838</FONT>                            break;<a name="line.838"></a>
<FONT color="green">839</FONT>                        }<a name="line.839"></a>
<FONT color="green">840</FONT>                    }<a name="line.840"></a>
<FONT color="green">841</FONT>                }<a name="line.841"></a>
<FONT color="green">842</FONT>                ++i;<a name="line.842"></a>
<FONT color="green">843</FONT>            }<a name="line.843"></a>
<FONT color="green">844</FONT>            return 0;<a name="line.844"></a>
<FONT color="green">845</FONT>        }<a name="line.845"></a>
<FONT color="green">846</FONT>    <a name="line.846"></a>
<FONT color="green">847</FONT>        /**<a name="line.847"></a>
<FONT color="green">848</FONT>         * Gets the diagonal of the Hat matrix also known as the leverage matrix.<a name="line.848"></a>
<FONT color="green">849</FONT>         *<a name="line.849"></a>
<FONT color="green">850</FONT>         * @param  row_data returns the diagonal of the hat matrix for this observation<a name="line.850"></a>
<FONT color="green">851</FONT>         * @return the diagonal element of the hatmatrix<a name="line.851"></a>
<FONT color="green">852</FONT>         */<a name="line.852"></a>
<FONT color="green">853</FONT>        public double getDiagonalOfHatMatrix(double[] row_data) {<a name="line.853"></a>
<FONT color="green">854</FONT>            double[] wk = new double[this.nvars];<a name="line.854"></a>
<FONT color="green">855</FONT>            int pos;<a name="line.855"></a>
<FONT color="green">856</FONT>            double total;<a name="line.856"></a>
<FONT color="green">857</FONT>    <a name="line.857"></a>
<FONT color="green">858</FONT>            if (row_data.length &gt; nvars) {<a name="line.858"></a>
<FONT color="green">859</FONT>                return Double.NaN;<a name="line.859"></a>
<FONT color="green">860</FONT>            }<a name="line.860"></a>
<FONT color="green">861</FONT>            double[] xrow;<a name="line.861"></a>
<FONT color="green">862</FONT>            if (this.hasIntercept) {<a name="line.862"></a>
<FONT color="green">863</FONT>                xrow = new double[row_data.length + 1];<a name="line.863"></a>
<FONT color="green">864</FONT>                xrow[0] = 1.0;<a name="line.864"></a>
<FONT color="green">865</FONT>                System.arraycopy(row_data, 0, xrow, 1, row_data.length);<a name="line.865"></a>
<FONT color="green">866</FONT>            } else {<a name="line.866"></a>
<FONT color="green">867</FONT>                xrow = row_data;<a name="line.867"></a>
<FONT color="green">868</FONT>            }<a name="line.868"></a>
<FONT color="green">869</FONT>            double hii = 0.0;<a name="line.869"></a>
<FONT color="green">870</FONT>            for (int col = 0; col &lt; xrow.length; col++) {<a name="line.870"></a>
<FONT color="green">871</FONT>                if (Math.sqrt(d[col]) &lt; tol[col]) {<a name="line.871"></a>
<FONT color="green">872</FONT>                    wk[col] = 0.0;<a name="line.872"></a>
<FONT color="green">873</FONT>                } else {<a name="line.873"></a>
<FONT color="green">874</FONT>                    pos = col - 1;<a name="line.874"></a>
<FONT color="green">875</FONT>                    total = xrow[col];<a name="line.875"></a>
<FONT color="green">876</FONT>                    for (int row = 0; row &lt; col; row++) {<a name="line.876"></a>
<FONT color="green">877</FONT>                        total = smartAdd(total, -wk[row] * r[pos]);<a name="line.877"></a>
<FONT color="green">878</FONT>                        pos += nvars - row - 2;<a name="line.878"></a>
<FONT color="green">879</FONT>                    }<a name="line.879"></a>
<FONT color="green">880</FONT>                    wk[col] = total;<a name="line.880"></a>
<FONT color="green">881</FONT>                    hii = smartAdd(hii, (total * total) / d[col]);<a name="line.881"></a>
<FONT color="green">882</FONT>                }<a name="line.882"></a>
<FONT color="green">883</FONT>            }<a name="line.883"></a>
<FONT color="green">884</FONT>            return hii;<a name="line.884"></a>
<FONT color="green">885</FONT>        }<a name="line.885"></a>
<FONT color="green">886</FONT>    <a name="line.886"></a>
<FONT color="green">887</FONT>        /**<a name="line.887"></a>
<FONT color="green">888</FONT>         * Gets the order of the regressors, useful if some type of reordering<a name="line.888"></a>
<FONT color="green">889</FONT>         * has been called. Calling regress with int[]{} args will trigger<a name="line.889"></a>
<FONT color="green">890</FONT>         * a reordering.<a name="line.890"></a>
<FONT color="green">891</FONT>         *<a name="line.891"></a>
<FONT color="green">892</FONT>         * @return int[] with the current order of the regressors<a name="line.892"></a>
<FONT color="green">893</FONT>         */<a name="line.893"></a>
<FONT color="green">894</FONT>        public int[] getOrderOfRegressors(){<a name="line.894"></a>
<FONT color="green">895</FONT>            return MathArrays.copyOf(vorder);<a name="line.895"></a>
<FONT color="green">896</FONT>        }<a name="line.896"></a>
<FONT color="green">897</FONT>    <a name="line.897"></a>
<FONT color="green">898</FONT>        /**<a name="line.898"></a>
<FONT color="green">899</FONT>         * Conducts a regression on the data in the model, using all regressors.<a name="line.899"></a>
<FONT color="green">900</FONT>         *<a name="line.900"></a>
<FONT color="green">901</FONT>         * @return RegressionResults the structure holding all regression results<a name="line.901"></a>
<FONT color="green">902</FONT>         * @exception  ModelSpecificationException - thrown if number of observations is<a name="line.902"></a>
<FONT color="green">903</FONT>         * less than the number of variables<a name="line.903"></a>
<FONT color="green">904</FONT>         */<a name="line.904"></a>
<FONT color="green">905</FONT>        public RegressionResults regress() throws ModelSpecificationException {<a name="line.905"></a>
<FONT color="green">906</FONT>            return regress(this.nvars);<a name="line.906"></a>
<FONT color="green">907</FONT>        }<a name="line.907"></a>
<FONT color="green">908</FONT>    <a name="line.908"></a>
<FONT color="green">909</FONT>        /**<a name="line.909"></a>
<FONT color="green">910</FONT>         * Conducts a regression on the data in the model, using a subset of regressors.<a name="line.910"></a>
<FONT color="green">911</FONT>         *<a name="line.911"></a>
<FONT color="green">912</FONT>         * @param numberOfRegressors many of the regressors to include (either in canonical<a name="line.912"></a>
<FONT color="green">913</FONT>         * order, or in the current reordered state)<a name="line.913"></a>
<FONT color="green">914</FONT>         * @return RegressionResults the structure holding all regression results<a name="line.914"></a>
<FONT color="green">915</FONT>         * @exception  ModelSpecificationException - thrown if number of observations is<a name="line.915"></a>
<FONT color="green">916</FONT>         * less than the number of variables or number of regressors requested<a name="line.916"></a>
<FONT color="green">917</FONT>         * is greater than the regressors in the model<a name="line.917"></a>
<FONT color="green">918</FONT>         */<a name="line.918"></a>
<FONT color="green">919</FONT>        public RegressionResults regress(int numberOfRegressors) throws ModelSpecificationException {<a name="line.919"></a>
<FONT color="green">920</FONT>            if (this.nobs &lt;= numberOfRegressors) {<a name="line.920"></a>
<FONT color="green">921</FONT>               throw new ModelSpecificationException(<a name="line.921"></a>
<FONT color="green">922</FONT>                       LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,<a name="line.922"></a>
<FONT color="green">923</FONT>                       this.nobs, numberOfRegressors);<a name="line.923"></a>
<FONT color="green">924</FONT>            }<a name="line.924"></a>
<FONT color="green">925</FONT>            if( numberOfRegressors &gt; this.nvars ){<a name="line.925"></a>
<FONT color="green">926</FONT>                throw new ModelSpecificationException(<a name="line.926"></a>
<FONT color="green">927</FONT>                        LocalizedFormats.TOO_MANY_REGRESSORS, numberOfRegressors, this.nvars);<a name="line.927"></a>
<FONT color="green">928</FONT>            }<a name="line.928"></a>
<FONT color="green">929</FONT>    <a name="line.929"></a>
<FONT color="green">930</FONT>            tolset();<a name="line.930"></a>
<FONT color="green">931</FONT>            singcheck();<a name="line.931"></a>
<FONT color="green">932</FONT>    <a name="line.932"></a>
<FONT color="green">933</FONT>            double[] beta = this.regcf(numberOfRegressors);<a name="line.933"></a>
<FONT color="green">934</FONT>    <a name="line.934"></a>
<FONT color="green">935</FONT>            ss();<a name="line.935"></a>
<FONT color="green">936</FONT>    <a name="line.936"></a>
<FONT color="green">937</FONT>            double[] cov = this.cov(numberOfRegressors);<a name="line.937"></a>
<FONT color="green">938</FONT>    <a name="line.938"></a>
<FONT color="green">939</FONT>            int rnk = 0;<a name="line.939"></a>
<FONT color="green">940</FONT>            for (int i = 0; i &lt; this.lindep.length; i++) {<a name="line.940"></a>
<FONT color="green">941</FONT>                if (!this.lindep[i]) {<a name="line.941"></a>
<FONT color="green">942</FONT>                    ++rnk;<a name="line.942"></a>
<FONT color="green">943</FONT>                }<a name="line.943"></a>
<FONT color="green">944</FONT>            }<a name="line.944"></a>
<FONT color="green">945</FONT>    <a name="line.945"></a>
<FONT color="green">946</FONT>            boolean needsReorder = false;<a name="line.946"></a>
<FONT color="green">947</FONT>            for (int i = 0; i &lt; numberOfRegressors; i++) {<a name="line.947"></a>
<FONT color="green">948</FONT>                if (this.vorder[i] != i) {<a name="line.948"></a>
<FONT color="green">949</FONT>                    needsReorder = true;<a name="line.949"></a>
<FONT color="green">950</FONT>                    break;<a name="line.950"></a>
<FONT color="green">951</FONT>                }<a name="line.951"></a>
<FONT color="green">952</FONT>            }<a name="line.952"></a>
<FONT color="green">953</FONT>            if (!needsReorder) {<a name="line.953"></a>
<FONT color="green">954</FONT>                return new RegressionResults(<a name="line.954"></a>
<FONT color="green">955</FONT>                        beta, new double[][]{cov}, true, this.nobs, rnk,<a name="line.955"></a>
<FONT color="green">956</FONT>                        this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);<a name="line.956"></a>
<FONT color="green">957</FONT>            } else {<a name="line.957"></a>
<FONT color="green">958</FONT>                double[] betaNew = new double[beta.length];<a name="line.958"></a>
<FONT color="green">959</FONT>                double[] covNew = new double[cov.length];<a name="line.959"></a>
<FONT color="green">960</FONT>    <a name="line.960"></a>
<FONT color="green">961</FONT>                int[] newIndices = new int[beta.length];<a name="line.961"></a>
<FONT color="green">962</FONT>                for (int i = 0; i &lt; nvars; i++) {<a name="line.962"></a>
<FONT color="green">963</FONT>                    for (int j = 0; j &lt; numberOfRegressors; j++) {<a name="line.963"></a>
<FONT color="green">964</FONT>                        if (this.vorder[j] == i) {<a name="line.964"></a>
<FONT color="green">965</FONT>                            betaNew[i] = beta[ j];<a name="line.965"></a>
<FONT color="green">966</FONT>                            newIndices[i] = j;<a name="line.966"></a>
<FONT color="green">967</FONT>                        }<a name="line.967"></a>
<FONT color="green">968</FONT>                    }<a name="line.968"></a>
<FONT color="green">969</FONT>                }<a name="line.969"></a>
<FONT color="green">970</FONT>    <a name="line.970"></a>
<FONT color="green">971</FONT>                int idx1 = 0;<a name="line.971"></a>
<FONT color="green">972</FONT>                int idx2;<a name="line.972"></a>
<FONT color="green">973</FONT>                int _i;<a name="line.973"></a>
<FONT color="green">974</FONT>                int _j;<a name="line.974"></a>
<FONT color="green">975</FONT>                for (int i = 0; i &lt; beta.length; i++) {<a name="line.975"></a>
<FONT color="green">976</FONT>                    _i = newIndices[i];<a name="line.976"></a>
<FONT color="green">977</FONT>                    for (int j = 0; j &lt;= i; j++, idx1++) {<a name="line.977"></a>
<FONT color="green">978</FONT>                        _j = newIndices[j];<a name="line.978"></a>
<FONT color="green">979</FONT>                        if (_i &gt; _j) {<a name="line.979"></a>
<FONT color="green">980</FONT>                            idx2 = _i * (_i + 1) / 2 + _j;<a name="line.980"></a>
<FONT color="green">981</FONT>                        } else {<a name="line.981"></a>
<FONT color="green">982</FONT>                            idx2 = _j * (_j + 1) / 2 + _i;<a name="line.982"></a>
<FONT color="green">983</FONT>                        }<a name="line.983"></a>
<FONT color="green">984</FONT>                        covNew[idx1] = cov[idx2];<a name="line.984"></a>
<FONT color="green">985</FONT>                    }<a name="line.985"></a>
<FONT color="green">986</FONT>                }<a name="line.986"></a>
<FONT color="green">987</FONT>                return new RegressionResults(<a name="line.987"></a>
<FONT color="green">988</FONT>                        betaNew, new double[][]{covNew}, true, this.nobs, rnk,<a name="line.988"></a>
<FONT color="green">989</FONT>                        this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);<a name="line.989"></a>
<FONT color="green">990</FONT>            }<a name="line.990"></a>
<FONT color="green">991</FONT>        }<a name="line.991"></a>
<FONT color="green">992</FONT>    <a name="line.992"></a>
<FONT color="green">993</FONT>        /**<a name="line.993"></a>
<FONT color="green">994</FONT>         * Conducts a regression on the data in the model, using regressors in array<a name="line.994"></a>
<FONT color="green">995</FONT>         * Calling this method will change the internal order of the regressors<a name="line.995"></a>
<FONT color="green">996</FONT>         * and care is required in interpreting the hatmatrix.<a name="line.996"></a>
<FONT color="green">997</FONT>         *<a name="line.997"></a>
<FONT color="green">998</FONT>         * @param  variablesToInclude array of variables to include in regression<a name="line.998"></a>
<FONT color="green">999</FONT>         * @return RegressionResults the structure holding all regression results<a name="line.999"></a>
<FONT color="green">1000</FONT>         * @exception  ModelSpecificationException - thrown if number of observations is<a name="line.1000"></a>
<FONT color="green">1001</FONT>         * less than the number of variables, the number of regressors requested<a name="line.1001"></a>
<FONT color="green">1002</FONT>         * is greater than the regressors in the model or a regressor index in<a name="line.1002"></a>
<FONT color="green">1003</FONT>         * regressor array does not exist<a name="line.1003"></a>
<FONT color="green">1004</FONT>         */<a name="line.1004"></a>
<FONT color="green">1005</FONT>        public RegressionResults regress(int[] variablesToInclude) throws ModelSpecificationException {<a name="line.1005"></a>
<FONT color="green">1006</FONT>            if (variablesToInclude.length &gt; this.nvars) {<a name="line.1006"></a>
<FONT color="green">1007</FONT>                throw new ModelSpecificationException(<a name="line.1007"></a>
<FONT color="green">1008</FONT>                        LocalizedFormats.TOO_MANY_REGRESSORS, variablesToInclude.length, this.nvars);<a name="line.1008"></a>
<FONT color="green">1009</FONT>            }<a name="line.1009"></a>
<FONT color="green">1010</FONT>            if (this.nobs &lt;= this.nvars) {<a name="line.1010"></a>
<FONT color="green">1011</FONT>                throw new ModelSpecificationException(<a name="line.1011"></a>
<FONT color="green">1012</FONT>                        LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,<a name="line.1012"></a>
<FONT color="green">1013</FONT>                        this.nobs, this.nvars);<a name="line.1013"></a>
<FONT color="green">1014</FONT>            }<a name="line.1014"></a>
<FONT color="green">1015</FONT>            Arrays.sort(variablesToInclude);<a name="line.1015"></a>
<FONT color="green">1016</FONT>            int iExclude = 0;<a name="line.1016"></a>
<FONT color="green">1017</FONT>            for (int i = 0; i &lt; variablesToInclude.length; i++) {<a name="line.1017"></a>
<FONT color="green">1018</FONT>                if (i &gt;= this.nvars) {<a name="line.1018"></a>
<FONT color="green">1019</FONT>                    throw new ModelSpecificationException(<a name="line.1019"></a>
<FONT color="green">1020</FONT>                            LocalizedFormats.INDEX_LARGER_THAN_MAX, i, this.nvars);<a name="line.1020"></a>
<FONT color="green">1021</FONT>                }<a name="line.1021"></a>
<FONT color="green">1022</FONT>                if (i &gt; 0 &amp;&amp; variablesToInclude[i] == variablesToInclude[i - 1]) {<a name="line.1022"></a>
<FONT color="green">1023</FONT>                    variablesToInclude[i] = -1;<a name="line.1023"></a>
<FONT color="green">1024</FONT>                    ++iExclude;<a name="line.1024"></a>
<FONT color="green">1025</FONT>                }<a name="line.1025"></a>
<FONT color="green">1026</FONT>            }<a name="line.1026"></a>
<FONT color="green">1027</FONT>            int[] series;<a name="line.1027"></a>
<FONT color="green">1028</FONT>            if (iExclude &gt; 0) {<a name="line.1028"></a>
<FONT color="green">1029</FONT>                int j = 0;<a name="line.1029"></a>
<FONT color="green">1030</FONT>                series = new int[variablesToInclude.length - iExclude];<a name="line.1030"></a>
<FONT color="green">1031</FONT>                for (int i = 0; i &lt; variablesToInclude.length; i++) {<a name="line.1031"></a>
<FONT color="green">1032</FONT>                    if (variablesToInclude[i] &gt; -1) {<a name="line.1032"></a>
<FONT color="green">1033</FONT>                        series[j] = variablesToInclude[i];<a name="line.1033"></a>
<FONT color="green">1034</FONT>                        ++j;<a name="line.1034"></a>
<FONT color="green">1035</FONT>                    }<a name="line.1035"></a>
<FONT color="green">1036</FONT>                }<a name="line.1036"></a>
<FONT color="green">1037</FONT>            } else {<a name="line.1037"></a>
<FONT color="green">1038</FONT>                series = variablesToInclude;<a name="line.1038"></a>
<FONT color="green">1039</FONT>            }<a name="line.1039"></a>
<FONT color="green">1040</FONT>    <a name="line.1040"></a>
<FONT color="green">1041</FONT>            reorderRegressors(series, 0);<a name="line.1041"></a>
<FONT color="green">1042</FONT>            tolset();<a name="line.1042"></a>
<FONT color="green">1043</FONT>            singcheck();<a name="line.1043"></a>
<FONT color="green">1044</FONT>    <a name="line.1044"></a>
<FONT color="green">1045</FONT>            double[] beta = this.regcf(series.length);<a name="line.1045"></a>
<FONT color="green">1046</FONT>    <a name="line.1046"></a>
<FONT color="green">1047</FONT>            ss();<a name="line.1047"></a>
<FONT color="green">1048</FONT>    <a name="line.1048"></a>
<FONT color="green">1049</FONT>            double[] cov = this.cov(series.length);<a name="line.1049"></a>
<FONT color="green">1050</FONT>    <a name="line.1050"></a>
<FONT color="green">1051</FONT>            int rnk = 0;<a name="line.1051"></a>
<FONT color="green">1052</FONT>            for (int i = 0; i &lt; this.lindep.length; i++) {<a name="line.1052"></a>
<FONT color="green">1053</FONT>                if (!this.lindep[i]) {<a name="line.1053"></a>
<FONT color="green">1054</FONT>                    ++rnk;<a name="line.1054"></a>
<FONT color="green">1055</FONT>                }<a name="line.1055"></a>
<FONT color="green">1056</FONT>            }<a name="line.1056"></a>
<FONT color="green">1057</FONT>    <a name="line.1057"></a>
<FONT color="green">1058</FONT>            boolean needsReorder = false;<a name="line.1058"></a>
<FONT color="green">1059</FONT>            for (int i = 0; i &lt; this.nvars; i++) {<a name="line.1059"></a>
<FONT color="green">1060</FONT>                if (this.vorder[i] != series[i]) {<a name="line.1060"></a>
<FONT color="green">1061</FONT>                    needsReorder = true;<a name="line.1061"></a>
<FONT color="green">1062</FONT>                    break;<a name="line.1062"></a>
<FONT color="green">1063</FONT>                }<a name="line.1063"></a>
<FONT color="green">1064</FONT>            }<a name="line.1064"></a>
<FONT color="green">1065</FONT>            if (!needsReorder) {<a name="line.1065"></a>
<FONT color="green">1066</FONT>                return new RegressionResults(<a name="line.1066"></a>
<FONT color="green">1067</FONT>                        beta, new double[][]{cov}, true, this.nobs, rnk,<a name="line.1067"></a>
<FONT color="green">1068</FONT>                        this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);<a name="line.1068"></a>
<FONT color="green">1069</FONT>            } else {<a name="line.1069"></a>
<FONT color="green">1070</FONT>                double[] betaNew = new double[beta.length];<a name="line.1070"></a>
<FONT color="green">1071</FONT>                int[] newIndices = new int[beta.length];<a name="line.1071"></a>
<FONT color="green">1072</FONT>                for (int i = 0; i &lt; series.length; i++) {<a name="line.1072"></a>
<FONT color="green">1073</FONT>                    for (int j = 0; j &lt; this.vorder.length; j++) {<a name="line.1073"></a>
<FONT color="green">1074</FONT>                        if (this.vorder[j] == series[i]) {<a name="line.1074"></a>
<FONT color="green">1075</FONT>                            betaNew[i] = beta[ j];<a name="line.1075"></a>
<FONT color="green">1076</FONT>                            newIndices[i] = j;<a name="line.1076"></a>
<FONT color="green">1077</FONT>                        }<a name="line.1077"></a>
<FONT color="green">1078</FONT>                    }<a name="line.1078"></a>
<FONT color="green">1079</FONT>                }<a name="line.1079"></a>
<FONT color="green">1080</FONT>                double[] covNew = new double[cov.length];<a name="line.1080"></a>
<FONT color="green">1081</FONT>                int idx1 = 0;<a name="line.1081"></a>
<FONT color="green">1082</FONT>                int idx2;<a name="line.1082"></a>
<FONT color="green">1083</FONT>                int _i;<a name="line.1083"></a>
<FONT color="green">1084</FONT>                int _j;<a name="line.1084"></a>
<FONT color="green">1085</FONT>                for (int i = 0; i &lt; beta.length; i++) {<a name="line.1085"></a>
<FONT color="green">1086</FONT>                    _i = newIndices[i];<a name="line.1086"></a>
<FONT color="green">1087</FONT>                    for (int j = 0; j &lt;= i; j++, idx1++) {<a name="line.1087"></a>
<FONT color="green">1088</FONT>                        _j = newIndices[j];<a name="line.1088"></a>
<FONT color="green">1089</FONT>                        if (_i &gt; _j) {<a name="line.1089"></a>
<FONT color="green">1090</FONT>                            idx2 = _i * (_i + 1) / 2 + _j;<a name="line.1090"></a>
<FONT color="green">1091</FONT>                        } else {<a name="line.1091"></a>
<FONT color="green">1092</FONT>                            idx2 = _j * (_j + 1) / 2 + _i;<a name="line.1092"></a>
<FONT color="green">1093</FONT>                        }<a name="line.1093"></a>
<FONT color="green">1094</FONT>                        covNew[idx1] = cov[idx2];<a name="line.1094"></a>
<FONT color="green">1095</FONT>                    }<a name="line.1095"></a>
<FONT color="green">1096</FONT>                }<a name="line.1096"></a>
<FONT color="green">1097</FONT>                return new RegressionResults(<a name="line.1097"></a>
<FONT color="green">1098</FONT>                        betaNew, new double[][]{covNew}, true, this.nobs, rnk,<a name="line.1098"></a>
<FONT color="green">1099</FONT>                        this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);<a name="line.1099"></a>
<FONT color="green">1100</FONT>            }<a name="line.1100"></a>
<FONT color="green">1101</FONT>        }<a name="line.1101"></a>
<FONT color="green">1102</FONT>    }<a name="line.1102"></a>




























































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